分类器组合技术研究及其在人机交互系统中的应用
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摘要
随着模式识别技术中遇到的实际问题的复杂化,单独分类器的性能已经难以满足许多实际应用的要求,分类器组合技术成为提高模式识别系统性能的一种新的重要手段。
分类器组合技术研究对于人机交互技术的发展具有重大意义。现代人机交互技术已经发展到多模态人机交互阶段,其中一个重要的课题是多模态识别。而分类器组合是解决多模态识别问题的一项关键技术。本文结合人机交互中的多模态识别问题,对四种分类器组合问题进行研究:(1)模式类别数量较多的分类器组合问题(类别数大于15);(2)模式类别数量较少的分类器组合问题(类别数在3～15之间);(3)两类分类器组合问题;(4)基于局部分类精度的分类器组合问题。
本文针对以上四类问题的特点提出了一些性能较高的分类器组合方法,主要研究成果如下:
1.针对模式类数量较多的分类器组合问题,提出了一类新的排序层分类器融合方法—序号变换法。这类方法将对基本分类器输出的模式类别排序号进行的变换与分类器的加权组合结合起来,从而在融合过程中能够增强小序号值对最终分类的影响。大量实验表明,序号变换法在分类正确率方面超过现有的排序层分类器融合方法0.1～1.0个百分点。
2.针对模式类数量较少的分类器组合问题,提出了一种新的度量层分类器融合方法:多决策模板法(MDT,Multiple Decision Templates)。其决策模板产生方法使得每个决策模板能够抑制一种容易发生的分类错误,从而增加少量决策模板就能够有效地提高分类正确率。在ELENA数据集与UCI数据集上的实验结果表明:与投票法、朴素贝叶斯法、线性融合规则及模板匹配法取得的最高分类正确率相比,该方法将分类正确率提高了0.4～0.9个百分点。与k-近邻规则相比,当训练样本较多时,二者的分类正确率相当,而MDT方法的计算量较小;当训练样本较少时,MDT方法能够取得较高的分类正确率。
3.针对两类分类器组合问题,提出了一种新的度量层分类器融合方法:基于类边界的分类器融合方法(CBCF,Class Boundary based Classifier Fusion)。该方法利用所研究问题在Meta层特征空间上的特点,直接从训练样本中提取类边界,然后基于边界点定义局部线性融合规则。在Phoneme数据集与Ringnorm数据集上的实验结果表明:与投票法、朴素贝叶斯法、模板匹配法及线性融合规则取得的最高分类正确率相比,CBCF方法将分类正确率提高了0.7～1.5个百分点;与k-近邻规则相比,二者的分类正确率很接近,而CBCF方法的计算量为k-近邻规则计算量的1/50～1/20。
4.针对基于局部分类精度的分类器组合问题,提出了一种根据局部分类精度估计分类置信度的方法,从而在解决该类问题时可以采用分类器融合方法代替传统的动态分类器选择方法,提高分类正确率。基于局部分类精度得到分类置信度以后,动态分类器选择等价于度量层分类器融合方法中的Max规则,从而采用性能更好的分类器融合方法能够提高分类正确率。ELENA数据集、UCI数据集与Ringnorm数据集上的大量实验结果表明该方法将分类正确率提高了0.2～13.6个百分点。
5.基于新的分类器组合方法设计了多模态身份识别系统与多模态身份认证系统。身份识别属于模式类数量较多的问题,所以在多模态身份识别系统中采用了序号变换法。身份认证可以作为两类问题处理,所以在多模态身份认证系统中采用了CBCF方法。实验表明本文方法显著提高了系统性能,使身份识别系统的正确识别率从94%(基本分类器正确识别率的最大值)提高到99.71%,身份认证系统的半错误率(HTER,Half Total Error Rate)从5.12%(基本分类器半错误率的最小值)下降到0.92%。
最后设计了多模态身份识别系统与多模态身份认证系统,为多模态人机交互中的应用打下了坚实的基础。
As the practical problems in the field of pattern recognition becomes more complex, the performance of one single classifier may not meet the demands in many real-world applications.Therefore,classifier combination has become a novel and important methodology to improve the performance of pattern recognition systems.
Research on classifier combination methods is crucial for the development of human-computer interaction.Modern human-computer interaction technology has developed into the stage of multi-modal human-computer interaction,in which multi-modal recognition is an important topic.Above all,classifier combination is a key technology to solve multi-modal recognition problems.In this dissertation,the theories and algorithms of classifier combination are studied for the multi-modal recognition problems in human-computer interaction.The following four kinds of classifier combination problems are studied:(1) classifier combination problems with a large number of classes(the number of classes is larger than 15);(2) classifier combination problems with a small number of classes(the number of classes is between 3 and 15);(3) two-class classifier combination problems;(4) local-accuracy based classifier combination problems.This dissertation makes the following achievements:
1.A novel kind of rank level classifier fusion method,named rank transformation method,is proposed for classifier combination problems with a large number of classes. By combining rank transformation with the weighted classifier fusion rule,the proposed method tries to increase the influence of the small rank values to the final classification results.Experimental results show that the proposed method outperforms traditional classifier combination methods by 0.1～1 percent in testing accuracy rate.
2.A new measurement level classifier fusion method,namly multiple decision templates(MDT),is proposed for measurement level classifier fusion problems with a small number of classes.The proposed method can increase the classification accuracy efficiently by using each decision template to reduce one kind of classification error. ELENA data sets and UCI data sets are used to test the MDT method,and the experimental results show the good performences of the proposed method.In comparison with the existing classifier combination method,such as voting,naive Bayesian,decision templates,the testing accuracy rate of the proposed MDT method is about 0.4-0.9 percent higher.Moreover,compared with the k-NN rule,the proposed method demonstrates the comparable testing accuracy rate and the low computation amount when the number of training samles is sufficiently large.When the number of training samples decreases,the proposed method can achieve higher testing accuracy rate
3.A new measurement level classifier fusion method named class-boundary based classifier fusion(CBCF) is proposed for two-class classifier combination problems. Based on the properties of the meta-level space of the considered problem,this method directly extracts the boundaries of the classes from the training set,and then defines local linear combination rules basing on the points on the boundaries.Experimental results on the Phoneme and Ringnorm data sets show that:compared with the existing classifier combination methods,such as voting,naive Bayesian,decision templates, and linear combination,the accuracy of the proposed CBCF method is 0.7～1.5 percent higher;compared with the k-NN rule,they are comparable in accuracy whereas the computational amount of the proposed method is 1/50-1/20 times than that of the k-NN rule.
4.Towards the local-accuracy based classifier combination problems,a method transforming local accuracy to classification confidence is proposed.Therefore,one can utilize classifier fusion algorithm to replace the traditional dynamic classifier selection methods to achieve higher accuracy.After the new method implemented,dynamic classifier selection is equivalent to the Max rule,so that using more effective classifier fusion methods can achieve higher accuracy.Experimental results on the Elena,UCI and Ringnorm data sets show that the proposed method can improve the accuracy of the multi-classifier systems by 0.2～13.6 percent higher.
5.A multi-modal person recognition system and a multi-modal person verification system are designed based on the new classifier combination methods.The multi-modal person recognition system uses a rank transformation method because it includes many classes.Meanwhile,the multi-modal person verification system includes only two classes,so the CBCF method is utilized.Experimental results on the multi-modal database show the better performances produced by the proposed classifier combination methods.The recognition rate of the person recognition system increases from 94%(the highest of the recognition rates of the base classifiers) to 99.71%,while the HTER(Half Total Error Rate) of the person verification system decreases from 5.12%(the least of the HTERs of the base classifiers) to 0.92%.
Finally,a multi-modal person recognition system and a multi-modal person verification system are designed.The research work lays a sound basis for applications in multi-modal human-computer interaction.
分类器组合技术研究对于人机交互技术的发展具有重大意义。现代人机交互技术已经发展到多模态人机交互阶段,其中一个重要的课题是多模态识别。而分类器组合是解决多模态识别问题的一项关键技术。本文结合人机交互中的多模态识别问题,对四种分类器组合问题进行研究:(1)模式类别数量较多的分类器组合问题(类别数大于15);(2)模式类别数量较少的分类器组合问题(类别数在3～15之间);(3)两类分类器组合问题;(4)基于局部分类精度的分类器组合问题。
本文针对以上四类问题的特点提出了一些性能较高的分类器组合方法,主要研究成果如下:
1.针对模式类数量较多的分类器组合问题,提出了一类新的排序层分类器融合方法—序号变换法。这类方法将对基本分类器输出的模式类别排序号进行的变换与分类器的加权组合结合起来,从而在融合过程中能够增强小序号值对最终分类的影响。大量实验表明,序号变换法在分类正确率方面超过现有的排序层分类器融合方法0.1～1.0个百分点。
2.针对模式类数量较少的分类器组合问题,提出了一种新的度量层分类器融合方法:多决策模板法(MDT,Multiple Decision Templates)。其决策模板产生方法使得每个决策模板能够抑制一种容易发生的分类错误,从而增加少量决策模板就能够有效地提高分类正确率。在ELENA数据集与UCI数据集上的实验结果表明:与投票法、朴素贝叶斯法、线性融合规则及模板匹配法取得的最高分类正确率相比,该方法将分类正确率提高了0.4～0.9个百分点。与k-近邻规则相比,当训练样本较多时,二者的分类正确率相当,而MDT方法的计算量较小;当训练样本较少时,MDT方法能够取得较高的分类正确率。
3.针对两类分类器组合问题,提出了一种新的度量层分类器融合方法:基于类边界的分类器融合方法(CBCF,Class Boundary based Classifier Fusion)。该方法利用所研究问题在Meta层特征空间上的特点,直接从训练样本中提取类边界,然后基于边界点定义局部线性融合规则。在Phoneme数据集与Ringnorm数据集上的实验结果表明:与投票法、朴素贝叶斯法、模板匹配法及线性融合规则取得的最高分类正确率相比,CBCF方法将分类正确率提高了0.7～1.5个百分点;与k-近邻规则相比,二者的分类正确率很接近,而CBCF方法的计算量为k-近邻规则计算量的1/50～1/20。
4.针对基于局部分类精度的分类器组合问题,提出了一种根据局部分类精度估计分类置信度的方法,从而在解决该类问题时可以采用分类器融合方法代替传统的动态分类器选择方法,提高分类正确率。基于局部分类精度得到分类置信度以后,动态分类器选择等价于度量层分类器融合方法中的Max规则,从而采用性能更好的分类器融合方法能够提高分类正确率。ELENA数据集、UCI数据集与Ringnorm数据集上的大量实验结果表明该方法将分类正确率提高了0.2～13.6个百分点。
5.基于新的分类器组合方法设计了多模态身份识别系统与多模态身份认证系统。身份识别属于模式类数量较多的问题,所以在多模态身份识别系统中采用了序号变换法。身份认证可以作为两类问题处理,所以在多模态身份认证系统中采用了CBCF方法。实验表明本文方法显著提高了系统性能,使身份识别系统的正确识别率从94%(基本分类器正确识别率的最大值)提高到99.71%,身份认证系统的半错误率(HTER,Half Total Error Rate)从5.12%(基本分类器半错误率的最小值)下降到0.92%。
最后设计了多模态身份识别系统与多模态身份认证系统,为多模态人机交互中的应用打下了坚实的基础。
As the practical problems in the field of pattern recognition becomes more complex, the performance of one single classifier may not meet the demands in many real-world applications.Therefore,classifier combination has become a novel and important methodology to improve the performance of pattern recognition systems.
Research on classifier combination methods is crucial for the development of human-computer interaction.Modern human-computer interaction technology has developed into the stage of multi-modal human-computer interaction,in which multi-modal recognition is an important topic.Above all,classifier combination is a key technology to solve multi-modal recognition problems.In this dissertation,the theories and algorithms of classifier combination are studied for the multi-modal recognition problems in human-computer interaction.The following four kinds of classifier combination problems are studied:(1) classifier combination problems with a large number of classes(the number of classes is larger than 15);(2) classifier combination problems with a small number of classes(the number of classes is between 3 and 15);(3) two-class classifier combination problems;(4) local-accuracy based classifier combination problems.This dissertation makes the following achievements:
1.A novel kind of rank level classifier fusion method,named rank transformation method,is proposed for classifier combination problems with a large number of classes. By combining rank transformation with the weighted classifier fusion rule,the proposed method tries to increase the influence of the small rank values to the final classification results.Experimental results show that the proposed method outperforms traditional classifier combination methods by 0.1～1 percent in testing accuracy rate.
2.A new measurement level classifier fusion method,namly multiple decision templates(MDT),is proposed for measurement level classifier fusion problems with a small number of classes.The proposed method can increase the classification accuracy efficiently by using each decision template to reduce one kind of classification error. ELENA data sets and UCI data sets are used to test the MDT method,and the experimental results show the good performences of the proposed method.In comparison with the existing classifier combination method,such as voting,naive Bayesian,decision templates,the testing accuracy rate of the proposed MDT method is about 0.4-0.9 percent higher.Moreover,compared with the k-NN rule,the proposed method demonstrates the comparable testing accuracy rate and the low computation amount when the number of training samles is sufficiently large.When the number of training samples decreases,the proposed method can achieve higher testing accuracy rate
3.A new measurement level classifier fusion method named class-boundary based classifier fusion(CBCF) is proposed for two-class classifier combination problems. Based on the properties of the meta-level space of the considered problem,this method directly extracts the boundaries of the classes from the training set,and then defines local linear combination rules basing on the points on the boundaries.Experimental results on the Phoneme and Ringnorm data sets show that:compared with the existing classifier combination methods,such as voting,naive Bayesian,decision templates, and linear combination,the accuracy of the proposed CBCF method is 0.7～1.5 percent higher;compared with the k-NN rule,they are comparable in accuracy whereas the computational amount of the proposed method is 1/50-1/20 times than that of the k-NN rule.
4.Towards the local-accuracy based classifier combination problems,a method transforming local accuracy to classification confidence is proposed.Therefore,one can utilize classifier fusion algorithm to replace the traditional dynamic classifier selection methods to achieve higher accuracy.After the new method implemented,dynamic classifier selection is equivalent to the Max rule,so that using more effective classifier fusion methods can achieve higher accuracy.Experimental results on the Elena,UCI and Ringnorm data sets show that the proposed method can improve the accuracy of the multi-classifier systems by 0.2～13.6 percent higher.
5.A multi-modal person recognition system and a multi-modal person verification system are designed based on the new classifier combination methods.The multi-modal person recognition system uses a rank transformation method because it includes many classes.Meanwhile,the multi-modal person verification system includes only two classes,so the CBCF method is utilized.Experimental results on the multi-modal database show the better performances produced by the proposed classifier combination methods.The recognition rate of the person recognition system increases from 94%(the highest of the recognition rates of the base classifiers) to 99.71%,while the HTER(Half Total Error Rate) of the person verification system decreases from 5.12%(the least of the HTERs of the base classifiers) to 0.92%.
Finally,a multi-modal person recognition system and a multi-modal person verification system are designed.The research work lays a sound basis for applications in multi-modal human-computer interaction.
引文
[1]J.Kittler,M.Hatef,R.P.W.Duin,and J.Matas.On combining classifiers.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(3):226-239.
[2]S.B.Cho,and J.H.Kim.Multiple network fusion using fuzzy logic,IEEE Transactions on Neural Networks,1995,6(2):497-501.
[3]K.Tumer,and J.Ghosh,Analysis of decision boundaries in linearly combined neural classifiers,Pattern Recognition,1996,29(2):341-348.
[4]J.Kittler,J.Matas,K.Jonsson,and M.U.Ramos Sanchez.Combining evidence in personal identity verification systems.Pattern Recognition Letters,1997,18(9):845-852.
[5]·L.Xu,A.Krzyzak,and Co Y.Suen.Methods of combining multiple classifiers and their applications to handwriting recognition,IEEE Transactions on Systems,Man,and Cybernetics,1992,22(3):418-435.
[6]M.Raza,I.Gonda,D.Green,and R.Coppel.Classifier fusion using dempster-shafer theory of evidence to predict breast cancer tumors.IEEE Region 10 Conference,2006.1-4.
[7]H.Altincay,and H.Demirekler.A novel rank-based classifier combination scheme for speaker identification.International Conference on Acoustics Speech and Signal Processing,2000.1209-1212.
[8]V.Chatzis,A.G.Bors,and I.Pitas.Multimodal Decision-Level Fusion for Person Authentication.IEEE Transactions on System,Man and Cybernetics -Part A:Systems and Humans,1999,29(6):674-680.
[9]P.S.Prampero,and A.C.P.L.De Carvalho,Classifier combination for vehicle silhouettes recognition,Seventh International Conference on Processing and its Applications,1999.67-71.
[10]A.Kumar,and D.Zhang,Integrating palmprint with face for user authentication.Proceedings of Workshop on Multimodal User Authentication.2003.107-112.
[11]S.Chitroub,A.Houacine,and B.Sansal.Evidential reasoning based classification method for remotely sensed images.Proceedings of SPIE,Image and signal processing for remote sensing conference,2001.340-351.
[12]S.Chitroub.Evidential reasoning based classifier combination for an optimal remote sensing image classification.2nd International Conference on Information and Communication Technologies.2006.337-342.
[13]F.Kimura,and M.Shridhar.Handwritten numerical recognition based on multiple algorithms.Pattern Recognition,1991,24(10):969-983.
[14]W.P.Kegelmeyer,and K.Bowyer.Combination of multiple classifiers using local accuracy estimates.IEEE Transactions on Pattern Analysis and Machine Intelligence.1997,19(4):405-410.
[15]L.Breiman.Bagging Predictors.Machine Learning,1996,24(2):123-140.
[16]Y.Freund,and R.E.Schapire.A decision-theoretic generalization of on-line leaming and an application to boosting.Journal of Computer and System Sciences,1997,55(1):119-139.
[17]袁保宗,阮秋琦,王延江,刘汝杰,唐晓芳.新一代(第四代)人机交互的概念框架特征及关键技术.电子学报,2003,31(12):1945-1954.
[18]王延江.新一代(第四代)人机交互的概念框架及其关键技术研究.北京交通大学博士学位论文.2001.
[19]B.V.Dasarathy.Sensor fusion potential exploitation:innovative architectures and illustrative applications.Proceedings of the IEEE,1997,85(1 ):24-38.
[20]D.L.Hall,and J.Llinas.An introduction to multisensor data fusion.Proceedings of the IEEE,1997,85(1):6-23.
[21]R.Sharma,V.I.Pavlovic,and T.S.Huang.Toward multimodal human-computer interface.Proceedings of the IEEE,1998,86(5):853-869.
[22]A.Kong,D.Zhang,and M.Kamel.Palmprint identification using feature-level fusion,Pattern Recognition,2006,39(3):478- 487.
[23]K.A.Toh.Fingerprint and speaker verification decisions fusion.International Conference on Image Analysis and Processing,2003.626-631.
[24]A.K.Jain,A.Ross,and S.Prabhakar.An Introduction to Biometric Recognition,IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):.4-20.
[25]A.Kale,A.K.Roychowdhury,and R.Chellappa.Fusion of gait and face for human identification.IEEE International Conference on Acoustics,Speech,and Signal Processing,2004.901-904.
[26]H.Ailisto,E.Vildjiounaite,M.Lindholm,S.M.Makela,and J.Peltola,Soft biometrics-combining body weight and fat measurements with fingerprint biometrics.Pattern recognition letters,2006,27(5):325-334.
[27]O.Melnik,Y.Vardi,and C.H.Zhang.Mixed group ranks:preference and confidence in classifier combination.IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):973-981.
[28]L.K.Hansen,and P.Salamon.Neural network ensembles.IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001.
[29]A.A.Spanjersberg.Combination of different systems for the recognition of handwritten digits.2nd International Joint Conference on Pattern Recognition,1974.208-209.
[30]M.G.Woolfson.Classifier integration with multiple sensors.Proceedings of the National Aerospace and Electronics Conference,1986.205-209.
[31]C.Y.Suen,C.Nadal,T.A.Mai,R.Legault and L.Lam.Recognition of totally unconstrained handwritten numerals based on the concept of multiple experts.Frontiers in Handwriting Recognition,C.Y.Suen ED..Proceedings of the International Workshop on Frontiers in Handwriting Recognitio,.1990.131-143.
[32]C.Nadal,R.Legault,and C.Y.Suen.Complementary algorithms for the recognition of totally unconstrained handwritten numerals.Proceedings of the 10th International Conference on Pattern Recognition,1990.434-449.
[33]J.J.Hull,A.Commike,and T.K.Ho.Multiple algorithms for handwritten character recognition.Frontiers in Handwriting Recognition,C.Y.Suen,Ed..Procedings of the International Workshop on Frontiers in Handwriting Recognition,1990.117-129.
[34]T.K.Ho,J.J.Hull,and S.N.Srihari.Combination of structural classifiers,Proceedings of the I990 IAPR Workshop on Syntactic and Structural Pattern Recognition,1990.123-137.
[35]R.Brunelli,D.Falavigna.Person identification using multiple cues.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(10):955-966.
[36]R.J.Liu,B.Z.Yuan.Multiple classifiers combination by clustering and selection.Information Fusion,2001,3(2):163-168.
[37]T.K.Ho.A Theory of multiple classifier systems and its application to visual word recognition.PhD Thesis.State University of New York at Buffalo,1992.
[38]M.Grabisch.,and J.M.Nicolas.Classification by fuzzy integral:performance and tests.Fuzzy Sets and Systems,1994,65(2-3):255-271.
[39]Y.S.Huang,and C.Y.Suen.A method of combining multiple experts for the recognition of unconstrained handwritten numerals.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(1):90-94.
[40]T.K.Ho,J.J.Hull,and S.N.Srihari.Decision combination in multiple classifier systems.IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(1):66-75.
[41]K.M.Ting,I.H.Witten.Issues in stacked generalization,Journal of Artificial Intelligence Research,1999,10:271-289.
[42]P.Verlinde,and G.Chollet.Combining vocal and visual cues in identification system using k-NN based classifiers.Proceedings of the IEEE Workshop on Multimedia Signal Processing.1998.59-64.
[43]N.Ueda.Optimal linear combination of neural networks for improving classification performance.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(2):207-215.
[44]L.I.Kuncheva.A theoretical study on six classifier fusion strategies.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(2):281-286.
[45]J.Kittler,and F.M.Alkoot.Sum versus vote fusion in multiple classifier systems.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(1):110-115.
[46]Y.H.Wang,T.N.Tan,and A.K.Jain.Combining face and iris biometrics for identity verification.Proceedings of the 4th International Conference on Audio and Video Based Person Authentication(AVBPA),2003.805-813.
[47]L.Wang,H.Z.Ning,T.N.Tan,and W.M.Hu.Fusion of static and dynamic body biometrics for gait recognition.IEEE Transactions on Circuits and Systems for Video Technology,2004,14(2):149-158.
[48]H.Zhu,and X.Tang.Classifier geometrical characteristic comparison and its application in classifier selection.Pattern recognition letters,2005,26(6):829 - 842.
[49]刘红毅,王蕴红,谭铁牛.基于改进ENN算法的多生物特征融合的身份验证.自动化学报,2004,30(1):78-85.
[50]刘汝杰,袁保宗,唐晓芳.用遗传算法实现模糊测度赋值的一种多分类器融合算法。电子学报,2002,30(1):145-147.
[51]刘汝杰.多功能感知中信息融合技术的研究.北京交通大学博士论文.
[52]L.Lam and C.Suen.Application of majority voting to pattern recognition:an analysis of its behavior and performance.IEEE Transactions on Systems,Man,and Cybernetics,Part A,1997,27(5):553-568.
[53]L.I.Kuncheva and C.J.Whitaker.Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,Machine Learning,2003,51(2) 181-207.
[54]N.Anand.Theoretical bounds of majority voting performance for a binary classification problem.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(12):1988-1995.
[55]L.I.Kuncheva.Combining Pattern Classifiers:Methods and Algorithms.John Wiley and Sons,2004.
[56]H.Altincay,On naive bayesian fusion of dependent classifiers.Pattern Recognition Letters,2005,26:2463-2473.
[57]Y.S.Huang,C.Y.Suen,The behavior-knowledge space method for combination of multiple classifiers.Computer Society Conference on Computer Vision and Pattern Recognition,1993.347-352.
[58]D.Black.The Theory of Committees and Elections.2nd ed..Cambridge University Press,1963.
[59]A.Agresti.Categorical Data Analysis.New York:Wiley,1990.
[60]A.Saranli,M.Demirekler.A statistical unified framework for rank-based multiple classifier decision combination.Pattern Recognition,2001,34(4):865-884.
[61]L.I.Kuncheva.Decision templates for multiple classifier fusion:an experimental comparison.Pattern Recognition,2001,34(2):299-314.
[62]L.I.Kuncheva,Using Measures of Similarity and Inclusion for Multiple Classifier Fusion by Decision Templates.Fuzzy Sets and Systems,2001,122(3):401-407.
[63]L.Zadeh,A simple view of the dempster - shafer theory of evidence and its implication for the rule of combination.AI Magazine,1986,7(2):85-90.
[64]G.Rogova.Combining the Results of Several Neural Network Classifiers.Neural Networks,1994,7(5):777-781
[65]G.S.Ng,and H.Singh.Data equalization with evidence combination for pattern recognition,Pattern Recognition Letters,1998,19(3):227-235.
[66]A.P.Dempster.Upper and lower probabilities induced by a multiple valued mapping.Annals of Mathematical Statistics,1967,38(2):325-339.
[67]G.Shafer.A mathematical theory of evidence.Princeton University Press.Princeton,1976.
[68]刘汝杰,袁保宗.一种考虑分类器不确定性的多通道D-S融合方法.铁道学报2000,22(4):42-45.
[69]J.M.Keller,P.Gader,H.Tahani,J.H.Chiang,and M.Mohamed.Advances in fuzzy integration for pattern recognition.Fuzzy Sets and Systems,1994,65(2-3),273-283.
[70]A.L.Buczak,R.E.Uhrig.Information Fusion by Fuzzy Set Operation and Genetic Algorithms.Simulation,1995,65(1):52-66.
[71]R.J.Liu,B.Z.Yuan,A D-S based multi-channel information fusion method using Classifiers'uncertainty measurement,Proceedings of 5~(th) IEEE International Conference on Signal Processing (ICSP '2000),2000.1297-1300.
[72]M.Sugeno.Fuzzy measures and fuzzy integrals - a survey.Fuzzy Automata and Decision Processes,Gupta M.M.,Saridis G.N.,Gaines B.R.Eds..1977.89-102
[73]D.Dubois,H.Prade.Properties of measures of information in evidence and possibility theories,Fuzzy Sets and Systems,1987,24(2):161-182
[74]郭桂蓉,庄钊文,信息处理中的模糊技术,国防科技大学出版社.1993.
[75]A.R.Mirhosseini,H.Yan,K.M.Lam,and T.Pham.Human Face Image Recognition:An Evidence Aggregation Approach.Computer Vision and Image Understanding,1998,71(2):213-230.
[76]T.Pham,and M.Wagner.Similarity Normalization for Speaker Verification by Fuzzy Fusion,Pattern Recognition,2000,33(2):309-315.
[77]T.Murofushi,M.Sugeno.An interpretation of fuzzy measure and the choquet integral as an integral with respect to a fuzzy measure.Fuzzy Sets and Systems,1989,29(2):201-227.
[78]Z.Chi,H.Yan,and T.D.Pham.Fuzzy Algorithm with Applications to Image Processing and Pattern Recognition.World Scientific Publish:1996.
[79]L.Breiman.Stacked Regressions.Machine Learning,1996,24(1):49-64.
[80]B.H.Juang,S.Katagiri,Discriminative learning for minimum error classification,IEEE Transactions on Signal Processing,1992,40(12):3043-3054.
[81]K.A.Toh.A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion.IEEE Transactions on Circuits and Systems for Video Technolog.2004,14(2):224 - 233.
[82]E.Frank,Y.Wang,S.Inglis,G.Holmes,and I.H.Witten,Using model trees for classification.Machine Learning,1998,32(1) 63-76.
[83]L.Todorovski,S.Dzeroski.Combining classifiers with meta decision trees.Machine Learning,2003,50(3):223-249.
[84]G.Giacinto,and F.Roll.A theoretical framework for dynamic classifier selection.Proceedings of International Conference on Pattern Recognition,2000.8-11.
[85]L.Didaci,G.Giacinto,F.Roli,and G.L.Marcialis.A study on the performances of dynamic classifier selection based on local accuracy estimation.Pattern Recognition,2005,38(11):2188-2191.
[86]J.J.Rodrignez,L.I.Kuncheva,and C.J.Alonso.Rotation forest:a new classifier ensemble method.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(10):1619- 1630.
[87]Y.Freund,and R.E.Schapire.A short introduction to boosting.Journal of Japanese Society for Artificial Intelligence,1999,14(5):771-780
[88]Y.Sun,Z.Liu,S.Todorovic,J.Li.Adaptive boosting for SAR automatic target recognition.IEEE transactions on Aerospace and Electronic Systems,2007,43(1):112 - 125.
[89]J.Lu,K.N.Plataniotis,A.N.Venetsanopoulos,and S.Z.Li.Ensemble-based discriminant learning with boosting for face recognition.IEEE Transactions Neural Networks,2006,17(1):166-178.
[90]U.Bagci,and E.Erzin.Boosting classifiers for music genre classification.IEEE 14~(th) Signal Processing and Communications Applications,2006.1-3.
[91]E.Grossmann.Adatree:boosting a weak classifier into a decision tree,IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,2004.105-112.
[92]B.O'Sullivan,A.Ferguson.,E.C.Freuder.Boosting constraint satisfaction using decision trees.16th IEEE International Conference on Tools with Artificial Intelligence,2004.646-651.
[93]C.L.Liu.Classifier combination based on confidence transformation.Pattern Recognition,2005,38(1):11-28.
[94]J.Kittler.A framework for classifier fusion:is it still needed? Advances in Pattern Recognition,Lecture Notes in Computer Science,Springer,2000,1876:45-56.
[95]X.Lin,X.Ding,M.Chen,R.Zhang,and Y.Wu,Adaptive confidence transform based classifier combination for Chinese character recognition.Pattern Recognition Letters,1998,19(10):975-988.
[96]A.S.Artukorale,and P.N.Suganthan.Combining classifiers based on confidence values.Proceedings of the Fifth International Conference on Document and Recognition,1999,37-40.
[97]R.Paredes and E.Vidal.Learning weighted metrics to minimize nearest-neighbor classification error.IEEE Trans.on Pattern Analysis and Machine Intelligence,2006,28(7):1100-1110.
[98]P.J.Phillips,H.Moon,S.A.Rizvi and P.J.Rauss.The Feret Evaluation Methodology for Face-Recognition Algorithms.IEEE Transactions on Pattern Analysis and Machine Intelligence.2000,22(10):1090 - 1104.
[99]A.K.Jian,R.Bolle,S.Pankanti.Biometrics:Personal Identification.Kluwer Academic Publish.1999.
[100]Q.Li,Z.D.Qiu,and D.M.Sun.Personal Identification Using Knuckleprint.Sinobiometric04,Lecture Notes in Computer Science,Springer.2004,3338:680-689,
[101]D.R.Wilson and T.R.Martinez.Reduction techniques for instance-based learning algorithms.Machine Learning,2000,38(3):257-286.
[102]P.Elzbieta,P.W.D.Robert,and P.Pavel.Prototype selection for dissimilarity-based classifiers.Pattern Recognition,2006,39(2):189-208.
[103]P.E.Hart.The condensed nearest neighbor rule.IEEE Transactions on Information Theory,1968,14(3):.515-516.
[104]G.W.Gates.The reduced nearest neighbor rule.IEEE Transactions on Information Theory,1972,18(3):431-433.
[105]J.P.Zhang.Selecting typical instances in instance-based learning.Proceedings of Ninth international conference on machine learning,1992..470-479.
[106]D.B.Skalak.Prototype and feature selection by sampling and random mutation hill climbing algorithm,proceedings of 11th international conference on machine learning,1994.291-301.
[107]P.Mitra,C.A.Murthy,and S.K.Pal.Density based multi-scale data condensation.IEEE Transactions on Pattern Analysis an-d Machine Intelligence.2002,24(6):734-747.
[108]C.L.Chang.Finding prototypes for nearest neighbor classifier.IEEE Transactions on Computer,1971,21(11):1179-1184.
[109]T.Kohonen.An introduction to neural computing.Neural Networks,1988,1(1):3-16.
[110]T.Kohonen.The self-organization map.Proceedings of the IEEE.1990,78:1468-1480.
[111]R.M.Gray.Vector quantization.IEEE ASSP Magzine,1984,1(2):.4-29.
[112]R.P.W.Duin,D.Ridder,and D.M.J.Tax.Experiments with a featureless approach to pattern recognition.Pattern Recognition Letters,1997,18(11 - 13):1159 - 1166.
[113]R.P.W.Duin,E.Pekalska,and D.de Ridder.Relational discriminant analysis.Pattern Recognition Letters,1999,20(11-13):1175 - 1181.
[114]V.Uebele,S.Abe,and M.S.Lan.A neural-network-based fuzzy classifier.IEEE Transactions on Systems,Man,and Cybernetics,1995,25(2):353-361.
[115]S.Abe,and R.Thawonmas,A fuzzy classifier with ellipsoidal regions.IEEE Transactions on Fuzzy Systems,1997,5(3):358-368.
[116]X.G.Chang,and J.H.Lilly.Evolutionary design of a fuzzy classifier from data.IEEE Transactions on Systems,Man,and Cybernetics,Part B,2004,34(4):1894-1996.
[117]Y.H.Chen,and J.Z.Wang.Support vector learning for fuzzy rule-based classification system.IEEE transactions on Fuzzy Systems,2003,11(6):716-728.
[118]L.X.Wang.,and J.M.Mendel.Generating Fuzzy Rules by Learning from Examples.IEEE Transactions on Systems,Man,and Cybernetics,1992,22(6):1414-1427.
[119]J.Abonyi,and F.Szeifert.Supervised fuzzy clustering for the identification of fuzzy classifiers.Pattern recognition letters,2002,24(14):2195 - 2207.
[120]边肇祺,张学工.模式识别,第2版.清华大学出版社.2000.
[121]M.M.Gupta,G.M.Trojan,and J.B.Kiszka,Controllability of Fuzzy Control Systems,IEEE Transactions on Systems,Man,and Cybernetics,1986,16(4):576-582.
[122]F.Cupertino,V.Giordano,D.Naso,and L.Delfine,Fuzzy control of a mobile robot,IEEE Robotics & Automation Magazine,2006,15(1):74-81.
[123]J.A.Dickerson,and B.Kosko.Fuzzy function approximation with ellipsoidal rules,IEEE Transactions on Systems,Man,and Cybernetics,Part B,1996,26(4):542-560.
[124]曾珂,张乃尧.线性T-S模型系统作为通用逼近器的充分条件.自动化学报,27(5):606-612
[125]S.Abe,R.Thawonmas,and M.Kayama,A fuzzy classifier with ellipsoidal regions for diagnosis problems,IEEE Transactions on Systems,Man,and Cybernetics,Part C,1999,29(1):817-835.
[126]S.C.Newton,S.Pemmaraju,S.Mitra,Adaptive fuzzy reader clustering of complex data sets in pattern recognition,IEEE Transactions on.Neural Networks,1992,3(5):794-800.
[127]L.I.Kuncheva.Switching between selection and fusion in combining classifiers:an experiment.IEEE Transactions.on Systems,Man,and Cybernetics,Part B,2002,32(2):146-156.
[128]D.Zhang,and W.Shu.Two novel characteristics in palmprint verification:datum point invariance and line feature matching.Pattern Recognition,1999,32(4):692-702.
[129]孙冬梅.手型和掌纹识别方法研究.北京交通大学博士论文.2003.
[130]S.Ribaric,and I.Fratric,A biometric identification system based on eigenpalm and eigenfinger features.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(11):1698-1709.
[131]K.Nandakumar.Integration of Multiple Cues in Biometric Systems.Marster dissertation.Michigan State University.2005.
[132]A.Ross,A.K.Jain,and J.Z.Qian.Information fusion in biometrics.Proceedings of the 3th International Conference on Audio and Video Based Person Authentication(AVBPA),2001.354-359.
[133]孙冬梅,裘正定.生物特征识别技术综述.电子学报,2001,29(12a):1744-1748.
[134]G.M.Lu,D.Zhang,and K.Wang.Pamprint recognition using Eigenpalms Features.Pattern Recognition Letters,2003,24(9):1463-1467.
[135]X.Wu,D.Zhang,and K.Wang,Fisherpalms bnased palmprint recognition,Pattern Recognition Letters,2003,24(15):2829-2838.
[136]O.Deniz.Castrillion,and Hernandez M,Face recognition using independent component analysis and support vector machines.Pattern Recognition Letters,2003,24(13):2153-2157.
[137]李文新.基于掌纹的在线身份验证.北京大学博士论文.2001.
[138]J.You,W.Li,and D.Zhang.Hierarchical palmprint identification via multiple feature extraction.Pattern Recognition,2002,35(4):.847-859.
[139]D.Zhang.Palmprint Authentication.Kluwer Acadenic 2004.
[140]戴青云,余英林,张大鹏.一种基于形态中值金字塔的线特征提取方法.华南理工大学学报(自然科学版.2001,29(5):14-18.
[141]N.Duta,and A.K.Jain.Mathing of palmprints.Pattern Recognition Letters,2002,23(4):477-485.
[142]李强.手部特征识别及特征级融合算法研究.北京交通大学博士学位论文.2006.
[143]M.M.Loeve.Probability Theory.Princeton,1955.
[144]L.Sirovich,and M.Kirby.Low dimentional procedure for the Characterization of human faces.Journal of the Optical Society of America,A,1987,4(3):519-524.
[145]M.Turk,and A.Pentland.Eigenfaces for recognition,Journal of Cognitive Neuralscience,1991, 3(1):71-86.
[146]M.Turk,and A.Pentland.Face recognition using eigenfaces.Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition,1991.586-591.
[147]J.Yang,D.Zhang,A.F.Frangi,J.Y.Yang,Two-dimentional PCA:a new approach to appearence-based face representation and recognition,IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(1):131-137.
[148]R.S.Reillo.Biometric identification through hand geometry measurements.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1168-1171.
[149]R.A.Fisher.The statistical utilization of multiple measurements.Annals of Eugenics.1938,8:376-386.
[150]R.A.Fisher.The use of multiple measures in taxonomic problems.Annals of Eugenics.1936,7:179-188.
[151]李铭.自动人脸检测与识别系统中若干问题的研究.北京交通大学博士学位论文.2005.
[152]D.L.Swets,and J.Weng.Using discriminant eigenfeatures for image retrieval.IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):831-836.
[153]A.M.Martinez,and A.C.Kak.PCA versus LDA.IEEE Transactions on Pattern Analysis and Machine Intelligence.2001,23(2):223-228.
[154]P.N.Belhumeur,J.P.Hespanha,and D.J.Kriegrnan.Eigenface vs.Fisherface:recognition using class specific linear projection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[155]R.Auckenthaler,M.Carey,and H.Lloyd-Tomas.Score normalization for text-independent speaker verification systems.Digital Signal Process,2000,10(1):42-54.
[156]F.A.Julian,O.G.Javier,and G.R.Joaquin.Target dependent score normalization techniques and their application to signature verification.IEEE Transactions on Systems,Man,and Cybernetics,Part C,2005,32(3):418-425.
[157]D.Reynolds.Comparison of background normalization methods for text-independent speaker verification.Eurospeech,1997.963-966.
[158]刘明,袁保宗,苗振江.一种新的双目标排序层分类器融合方法.自动化学报,2007,33(12):1276-1282.
[159]Image Processing Toolbox User's Guide,The Math Works Inc.,2001.
[160]C.C.Han,H.Chen,C.Lin,and K.Fan.Person authentification using Palmprint features.Pattern Recognition,2003,32(2):371-381.
[161]D.Zhang,W.K.Kong,and J.You.Online Palmprint verification.IEEE Transactions on Pattern Analysis andMachine Intelligence.2003,25(9):1041-1050.
[162]Y.Freund and R.Schapire.Experiments with a new boosting algorithm.Proceedings of the 13th International Conference on Machine Learning,1996.149-56.
[163]D.S.Lee,S.N Srihari.A theory of classifier combination:the neural network approach.Proceedings of the Third International Conference on Document Analysis and Recognition,1995. 42-45.
[164]P.Muneesawang,and L.Guan,SVM-Based decision fusion model for detecting concepts in films,6th International Conference on Information,Communications,and Signal Processing,2007.1-5.
[165]A.Sharkey,and N.Sharkey.Combining diverse neural networks.The Knowledge Engineering Review.1997.
[166]P.Cunningham,and J.Carney Diversity versus quality in classification ensembles based on feature selection.European Conference on Machine Learning,2000.109-116.
[167]L.I.Kuncheva,C.Whitaker,C.Shipp,and R.Duin.Is independence good for combining classifiers.Proceedings of the 15th International Conference on Pattern Recognition,2000,2:168-171.
[168]Z.H.Zhou,Y.Yang,Ensembling local learners trough multimodal perturbation,IEEE Transactions on Systems,Man,and Cybernetics,Part B,2005,35(4):725-735.
[169]Z.H.Zhou,and M.Li.Semisupervised regression with co-training,Proceedings of the 19th International Joint Conference on Artificial Intelligence,2005.908-913.
[170]Z.H.Zhou,and M.Li.,Semisupervised regression with co-training style algorithms.IEEE Transactions on Knowledge and Data Engineering,2007,19(11):1479-1493
[171]A.Asuncion,D.J.Newman.UCI Machine Learning Repository [http://www.ics.uci.edu/～mlearn/MLRepository.html].Irvine,CA:University of California,School of Information and Computer Science.2007.
[172]L.Breiman.Arcing classifiers.The Annals of Statistics,1998,26(3):801-849.
[173]P.Brodatz.Textures:A Photographic Album for Artists and Designers.Dover Publications,1966.
[174]L.Breiman,J.H.Friedman,R.A.Olshen,and C.J.Stone.Classification and regression trees.Wadsworth,1984.
[175]Neural Network Group.The ELENA Project[ftp.dice.ucl.ac.be/pub/neural-ets/].2005
[176]A.K.Jain,K.Nandakumar,and A.Ross.Score normalization in multimodal biometric systems.Pattern Recognition,2005,38(12):2270 - 2285.
[177]朱辉,多分类器系统关键技术及应用研究,哈尔滨工业大学博士论文,2007.
[178]R.Hu,R.I.Damper,A 'no panacea theorem ' for classifier combination,Pattern Recognition,2008,41(8):2665-2673.
[2]S.B.Cho,and J.H.Kim.Multiple network fusion using fuzzy logic,IEEE Transactions on Neural Networks,1995,6(2):497-501.
[3]K.Tumer,and J.Ghosh,Analysis of decision boundaries in linearly combined neural classifiers,Pattern Recognition,1996,29(2):341-348.
[4]J.Kittler,J.Matas,K.Jonsson,and M.U.Ramos Sanchez.Combining evidence in personal identity verification systems.Pattern Recognition Letters,1997,18(9):845-852.
[5]·L.Xu,A.Krzyzak,and Co Y.Suen.Methods of combining multiple classifiers and their applications to handwriting recognition,IEEE Transactions on Systems,Man,and Cybernetics,1992,22(3):418-435.
[6]M.Raza,I.Gonda,D.Green,and R.Coppel.Classifier fusion using dempster-shafer theory of evidence to predict breast cancer tumors.IEEE Region 10 Conference,2006.1-4.
[7]H.Altincay,and H.Demirekler.A novel rank-based classifier combination scheme for speaker identification.International Conference on Acoustics Speech and Signal Processing,2000.1209-1212.
[8]V.Chatzis,A.G.Bors,and I.Pitas.Multimodal Decision-Level Fusion for Person Authentication.IEEE Transactions on System,Man and Cybernetics -Part A:Systems and Humans,1999,29(6):674-680.
[9]P.S.Prampero,and A.C.P.L.De Carvalho,Classifier combination for vehicle silhouettes recognition,Seventh International Conference on Processing and its Applications,1999.67-71.
[10]A.Kumar,and D.Zhang,Integrating palmprint with face for user authentication.Proceedings of Workshop on Multimodal User Authentication.2003.107-112.
[11]S.Chitroub,A.Houacine,and B.Sansal.Evidential reasoning based classification method for remotely sensed images.Proceedings of SPIE,Image and signal processing for remote sensing conference,2001.340-351.
[12]S.Chitroub.Evidential reasoning based classifier combination for an optimal remote sensing image classification.2nd International Conference on Information and Communication Technologies.2006.337-342.
[13]F.Kimura,and M.Shridhar.Handwritten numerical recognition based on multiple algorithms.Pattern Recognition,1991,24(10):969-983.
[14]W.P.Kegelmeyer,and K.Bowyer.Combination of multiple classifiers using local accuracy estimates.IEEE Transactions on Pattern Analysis and Machine Intelligence.1997,19(4):405-410.
[15]L.Breiman.Bagging Predictors.Machine Learning,1996,24(2):123-140.
[16]Y.Freund,and R.E.Schapire.A decision-theoretic generalization of on-line leaming and an application to boosting.Journal of Computer and System Sciences,1997,55(1):119-139.
[17]袁保宗,阮秋琦,王延江,刘汝杰,唐晓芳.新一代(第四代)人机交互的概念框架特征及关键技术.电子学报,2003,31(12):1945-1954.
[18]王延江.新一代(第四代)人机交互的概念框架及其关键技术研究.北京交通大学博士学位论文.2001.
[19]B.V.Dasarathy.Sensor fusion potential exploitation:innovative architectures and illustrative applications.Proceedings of the IEEE,1997,85(1 ):24-38.
[20]D.L.Hall,and J.Llinas.An introduction to multisensor data fusion.Proceedings of the IEEE,1997,85(1):6-23.
[21]R.Sharma,V.I.Pavlovic,and T.S.Huang.Toward multimodal human-computer interface.Proceedings of the IEEE,1998,86(5):853-869.
[22]A.Kong,D.Zhang,and M.Kamel.Palmprint identification using feature-level fusion,Pattern Recognition,2006,39(3):478- 487.
[23]K.A.Toh.Fingerprint and speaker verification decisions fusion.International Conference on Image Analysis and Processing,2003.626-631.
[24]A.K.Jain,A.Ross,and S.Prabhakar.An Introduction to Biometric Recognition,IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):.4-20.
[25]A.Kale,A.K.Roychowdhury,and R.Chellappa.Fusion of gait and face for human identification.IEEE International Conference on Acoustics,Speech,and Signal Processing,2004.901-904.
[26]H.Ailisto,E.Vildjiounaite,M.Lindholm,S.M.Makela,and J.Peltola,Soft biometrics-combining body weight and fat measurements with fingerprint biometrics.Pattern recognition letters,2006,27(5):325-334.
[27]O.Melnik,Y.Vardi,and C.H.Zhang.Mixed group ranks:preference and confidence in classifier combination.IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):973-981.
[28]L.K.Hansen,and P.Salamon.Neural network ensembles.IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001.
[29]A.A.Spanjersberg.Combination of different systems for the recognition of handwritten digits.2nd International Joint Conference on Pattern Recognition,1974.208-209.
[30]M.G.Woolfson.Classifier integration with multiple sensors.Proceedings of the National Aerospace and Electronics Conference,1986.205-209.
[31]C.Y.Suen,C.Nadal,T.A.Mai,R.Legault and L.Lam.Recognition of totally unconstrained handwritten numerals based on the concept of multiple experts.Frontiers in Handwriting Recognition,C.Y.Suen ED..Proceedings of the International Workshop on Frontiers in Handwriting Recognitio,.1990.131-143.
[32]C.Nadal,R.Legault,and C.Y.Suen.Complementary algorithms for the recognition of totally unconstrained handwritten numerals.Proceedings of the 10th International Conference on Pattern Recognition,1990.434-449.
[33]J.J.Hull,A.Commike,and T.K.Ho.Multiple algorithms for handwritten character recognition.Frontiers in Handwriting Recognition,C.Y.Suen,Ed..Procedings of the International Workshop on Frontiers in Handwriting Recognition,1990.117-129.
[34]T.K.Ho,J.J.Hull,and S.N.Srihari.Combination of structural classifiers,Proceedings of the I990 IAPR Workshop on Syntactic and Structural Pattern Recognition,1990.123-137.
[35]R.Brunelli,D.Falavigna.Person identification using multiple cues.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(10):955-966.
[36]R.J.Liu,B.Z.Yuan.Multiple classifiers combination by clustering and selection.Information Fusion,2001,3(2):163-168.
[37]T.K.Ho.A Theory of multiple classifier systems and its application to visual word recognition.PhD Thesis.State University of New York at Buffalo,1992.
[38]M.Grabisch.,and J.M.Nicolas.Classification by fuzzy integral:performance and tests.Fuzzy Sets and Systems,1994,65(2-3):255-271.
[39]Y.S.Huang,and C.Y.Suen.A method of combining multiple experts for the recognition of unconstrained handwritten numerals.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(1):90-94.
[40]T.K.Ho,J.J.Hull,and S.N.Srihari.Decision combination in multiple classifier systems.IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(1):66-75.
[41]K.M.Ting,I.H.Witten.Issues in stacked generalization,Journal of Artificial Intelligence Research,1999,10:271-289.
[42]P.Verlinde,and G.Chollet.Combining vocal and visual cues in identification system using k-NN based classifiers.Proceedings of the IEEE Workshop on Multimedia Signal Processing.1998.59-64.
[43]N.Ueda.Optimal linear combination of neural networks for improving classification performance.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(2):207-215.
[44]L.I.Kuncheva.A theoretical study on six classifier fusion strategies.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(2):281-286.
[45]J.Kittler,and F.M.Alkoot.Sum versus vote fusion in multiple classifier systems.IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(1):110-115.
[46]Y.H.Wang,T.N.Tan,and A.K.Jain.Combining face and iris biometrics for identity verification.Proceedings of the 4th International Conference on Audio and Video Based Person Authentication(AVBPA),2003.805-813.
[47]L.Wang,H.Z.Ning,T.N.Tan,and W.M.Hu.Fusion of static and dynamic body biometrics for gait recognition.IEEE Transactions on Circuits and Systems for Video Technology,2004,14(2):149-158.
[48]H.Zhu,and X.Tang.Classifier geometrical characteristic comparison and its application in classifier selection.Pattern recognition letters,2005,26(6):829 - 842.
[49]刘红毅,王蕴红,谭铁牛.基于改进ENN算法的多生物特征融合的身份验证.自动化学报,2004,30(1):78-85.
[50]刘汝杰,袁保宗,唐晓芳.用遗传算法实现模糊测度赋值的一种多分类器融合算法。电子学报,2002,30(1):145-147.
[51]刘汝杰.多功能感知中信息融合技术的研究.北京交通大学博士论文.
[52]L.Lam and C.Suen.Application of majority voting to pattern recognition:an analysis of its behavior and performance.IEEE Transactions on Systems,Man,and Cybernetics,Part A,1997,27(5):553-568.
[53]L.I.Kuncheva and C.J.Whitaker.Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,Machine Learning,2003,51(2) 181-207.
[54]N.Anand.Theoretical bounds of majority voting performance for a binary classification problem.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(12):1988-1995.
[55]L.I.Kuncheva.Combining Pattern Classifiers:Methods and Algorithms.John Wiley and Sons,2004.
[56]H.Altincay,On naive bayesian fusion of dependent classifiers.Pattern Recognition Letters,2005,26:2463-2473.
[57]Y.S.Huang,C.Y.Suen,The behavior-knowledge space method for combination of multiple classifiers.Computer Society Conference on Computer Vision and Pattern Recognition,1993.347-352.
[58]D.Black.The Theory of Committees and Elections.2nd ed..Cambridge University Press,1963.
[59]A.Agresti.Categorical Data Analysis.New York:Wiley,1990.
[60]A.Saranli,M.Demirekler.A statistical unified framework for rank-based multiple classifier decision combination.Pattern Recognition,2001,34(4):865-884.
[61]L.I.Kuncheva.Decision templates for multiple classifier fusion:an experimental comparison.Pattern Recognition,2001,34(2):299-314.
[62]L.I.Kuncheva,Using Measures of Similarity and Inclusion for Multiple Classifier Fusion by Decision Templates.Fuzzy Sets and Systems,2001,122(3):401-407.
[63]L.Zadeh,A simple view of the dempster - shafer theory of evidence and its implication for the rule of combination.AI Magazine,1986,7(2):85-90.
[64]G.Rogova.Combining the Results of Several Neural Network Classifiers.Neural Networks,1994,7(5):777-781
[65]G.S.Ng,and H.Singh.Data equalization with evidence combination for pattern recognition,Pattern Recognition Letters,1998,19(3):227-235.
[66]A.P.Dempster.Upper and lower probabilities induced by a multiple valued mapping.Annals of Mathematical Statistics,1967,38(2):325-339.
[67]G.Shafer.A mathematical theory of evidence.Princeton University Press.Princeton,1976.
[68]刘汝杰,袁保宗.一种考虑分类器不确定性的多通道D-S融合方法.铁道学报2000,22(4):42-45.
[69]J.M.Keller,P.Gader,H.Tahani,J.H.Chiang,and M.Mohamed.Advances in fuzzy integration for pattern recognition.Fuzzy Sets and Systems,1994,65(2-3),273-283.
[70]A.L.Buczak,R.E.Uhrig.Information Fusion by Fuzzy Set Operation and Genetic Algorithms.Simulation,1995,65(1):52-66.
[71]R.J.Liu,B.Z.Yuan,A D-S based multi-channel information fusion method using Classifiers'uncertainty measurement,Proceedings of 5~(th) IEEE International Conference on Signal Processing (ICSP '2000),2000.1297-1300.
[72]M.Sugeno.Fuzzy measures and fuzzy integrals - a survey.Fuzzy Automata and Decision Processes,Gupta M.M.,Saridis G.N.,Gaines B.R.Eds..1977.89-102
[73]D.Dubois,H.Prade.Properties of measures of information in evidence and possibility theories,Fuzzy Sets and Systems,1987,24(2):161-182
[74]郭桂蓉,庄钊文,信息处理中的模糊技术,国防科技大学出版社.1993.
[75]A.R.Mirhosseini,H.Yan,K.M.Lam,and T.Pham.Human Face Image Recognition:An Evidence Aggregation Approach.Computer Vision and Image Understanding,1998,71(2):213-230.
[76]T.Pham,and M.Wagner.Similarity Normalization for Speaker Verification by Fuzzy Fusion,Pattern Recognition,2000,33(2):309-315.
[77]T.Murofushi,M.Sugeno.An interpretation of fuzzy measure and the choquet integral as an integral with respect to a fuzzy measure.Fuzzy Sets and Systems,1989,29(2):201-227.
[78]Z.Chi,H.Yan,and T.D.Pham.Fuzzy Algorithm with Applications to Image Processing and Pattern Recognition.World Scientific Publish:1996.
[79]L.Breiman.Stacked Regressions.Machine Learning,1996,24(1):49-64.
[80]B.H.Juang,S.Katagiri,Discriminative learning for minimum error classification,IEEE Transactions on Signal Processing,1992,40(12):3043-3054.
[81]K.A.Toh.A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion.IEEE Transactions on Circuits and Systems for Video Technolog.2004,14(2):224 - 233.
[82]E.Frank,Y.Wang,S.Inglis,G.Holmes,and I.H.Witten,Using model trees for classification.Machine Learning,1998,32(1) 63-76.
[83]L.Todorovski,S.Dzeroski.Combining classifiers with meta decision trees.Machine Learning,2003,50(3):223-249.
[84]G.Giacinto,and F.Roll.A theoretical framework for dynamic classifier selection.Proceedings of International Conference on Pattern Recognition,2000.8-11.
[85]L.Didaci,G.Giacinto,F.Roli,and G.L.Marcialis.A study on the performances of dynamic classifier selection based on local accuracy estimation.Pattern Recognition,2005,38(11):2188-2191.
[86]J.J.Rodrignez,L.I.Kuncheva,and C.J.Alonso.Rotation forest:a new classifier ensemble method.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(10):1619- 1630.
[87]Y.Freund,and R.E.Schapire.A short introduction to boosting.Journal of Japanese Society for Artificial Intelligence,1999,14(5):771-780
[88]Y.Sun,Z.Liu,S.Todorovic,J.Li.Adaptive boosting for SAR automatic target recognition.IEEE transactions on Aerospace and Electronic Systems,2007,43(1):112 - 125.
[89]J.Lu,K.N.Plataniotis,A.N.Venetsanopoulos,and S.Z.Li.Ensemble-based discriminant learning with boosting for face recognition.IEEE Transactions Neural Networks,2006,17(1):166-178.
[90]U.Bagci,and E.Erzin.Boosting classifiers for music genre classification.IEEE 14~(th) Signal Processing and Communications Applications,2006.1-3.
[91]E.Grossmann.Adatree:boosting a weak classifier into a decision tree,IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,2004.105-112.
[92]B.O'Sullivan,A.Ferguson.,E.C.Freuder.Boosting constraint satisfaction using decision trees.16th IEEE International Conference on Tools with Artificial Intelligence,2004.646-651.
[93]C.L.Liu.Classifier combination based on confidence transformation.Pattern Recognition,2005,38(1):11-28.
[94]J.Kittler.A framework for classifier fusion:is it still needed? Advances in Pattern Recognition,Lecture Notes in Computer Science,Springer,2000,1876:45-56.
[95]X.Lin,X.Ding,M.Chen,R.Zhang,and Y.Wu,Adaptive confidence transform based classifier combination for Chinese character recognition.Pattern Recognition Letters,1998,19(10):975-988.
[96]A.S.Artukorale,and P.N.Suganthan.Combining classifiers based on confidence values.Proceedings of the Fifth International Conference on Document and Recognition,1999,37-40.
[97]R.Paredes and E.Vidal.Learning weighted metrics to minimize nearest-neighbor classification error.IEEE Trans.on Pattern Analysis and Machine Intelligence,2006,28(7):1100-1110.
[98]P.J.Phillips,H.Moon,S.A.Rizvi and P.J.Rauss.The Feret Evaluation Methodology for Face-Recognition Algorithms.IEEE Transactions on Pattern Analysis and Machine Intelligence.2000,22(10):1090 - 1104.
[99]A.K.Jian,R.Bolle,S.Pankanti.Biometrics:Personal Identification.Kluwer Academic Publish.1999.
[100]Q.Li,Z.D.Qiu,and D.M.Sun.Personal Identification Using Knuckleprint.Sinobiometric04,Lecture Notes in Computer Science,Springer.2004,3338:680-689,
[101]D.R.Wilson and T.R.Martinez.Reduction techniques for instance-based learning algorithms.Machine Learning,2000,38(3):257-286.
[102]P.Elzbieta,P.W.D.Robert,and P.Pavel.Prototype selection for dissimilarity-based classifiers.Pattern Recognition,2006,39(2):189-208.
[103]P.E.Hart.The condensed nearest neighbor rule.IEEE Transactions on Information Theory,1968,14(3):.515-516.
[104]G.W.Gates.The reduced nearest neighbor rule.IEEE Transactions on Information Theory,1972,18(3):431-433.
[105]J.P.Zhang.Selecting typical instances in instance-based learning.Proceedings of Ninth international conference on machine learning,1992..470-479.
[106]D.B.Skalak.Prototype and feature selection by sampling and random mutation hill climbing algorithm,proceedings of 11th international conference on machine learning,1994.291-301.
[107]P.Mitra,C.A.Murthy,and S.K.Pal.Density based multi-scale data condensation.IEEE Transactions on Pattern Analysis an-d Machine Intelligence.2002,24(6):734-747.
[108]C.L.Chang.Finding prototypes for nearest neighbor classifier.IEEE Transactions on Computer,1971,21(11):1179-1184.
[109]T.Kohonen.An introduction to neural computing.Neural Networks,1988,1(1):3-16.
[110]T.Kohonen.The self-organization map.Proceedings of the IEEE.1990,78:1468-1480.
[111]R.M.Gray.Vector quantization.IEEE ASSP Magzine,1984,1(2):.4-29.
[112]R.P.W.Duin,D.Ridder,and D.M.J.Tax.Experiments with a featureless approach to pattern recognition.Pattern Recognition Letters,1997,18(11 - 13):1159 - 1166.
[113]R.P.W.Duin,E.Pekalska,and D.de Ridder.Relational discriminant analysis.Pattern Recognition Letters,1999,20(11-13):1175 - 1181.
[114]V.Uebele,S.Abe,and M.S.Lan.A neural-network-based fuzzy classifier.IEEE Transactions on Systems,Man,and Cybernetics,1995,25(2):353-361.
[115]S.Abe,and R.Thawonmas,A fuzzy classifier with ellipsoidal regions.IEEE Transactions on Fuzzy Systems,1997,5(3):358-368.
[116]X.G.Chang,and J.H.Lilly.Evolutionary design of a fuzzy classifier from data.IEEE Transactions on Systems,Man,and Cybernetics,Part B,2004,34(4):1894-1996.
[117]Y.H.Chen,and J.Z.Wang.Support vector learning for fuzzy rule-based classification system.IEEE transactions on Fuzzy Systems,2003,11(6):716-728.
[118]L.X.Wang.,and J.M.Mendel.Generating Fuzzy Rules by Learning from Examples.IEEE Transactions on Systems,Man,and Cybernetics,1992,22(6):1414-1427.
[119]J.Abonyi,and F.Szeifert.Supervised fuzzy clustering for the identification of fuzzy classifiers.Pattern recognition letters,2002,24(14):2195 - 2207.
[120]边肇祺,张学工.模式识别,第2版.清华大学出版社.2000.
[121]M.M.Gupta,G.M.Trojan,and J.B.Kiszka,Controllability of Fuzzy Control Systems,IEEE Transactions on Systems,Man,and Cybernetics,1986,16(4):576-582.
[122]F.Cupertino,V.Giordano,D.Naso,and L.Delfine,Fuzzy control of a mobile robot,IEEE Robotics & Automation Magazine,2006,15(1):74-81.
[123]J.A.Dickerson,and B.Kosko.Fuzzy function approximation with ellipsoidal rules,IEEE Transactions on Systems,Man,and Cybernetics,Part B,1996,26(4):542-560.
[124]曾珂,张乃尧.线性T-S模型系统作为通用逼近器的充分条件.自动化学报,27(5):606-612
[125]S.Abe,R.Thawonmas,and M.Kayama,A fuzzy classifier with ellipsoidal regions for diagnosis problems,IEEE Transactions on Systems,Man,and Cybernetics,Part C,1999,29(1):817-835.
[126]S.C.Newton,S.Pemmaraju,S.Mitra,Adaptive fuzzy reader clustering of complex data sets in pattern recognition,IEEE Transactions on.Neural Networks,1992,3(5):794-800.
[127]L.I.Kuncheva.Switching between selection and fusion in combining classifiers:an experiment.IEEE Transactions.on Systems,Man,and Cybernetics,Part B,2002,32(2):146-156.
[128]D.Zhang,and W.Shu.Two novel characteristics in palmprint verification:datum point invariance and line feature matching.Pattern Recognition,1999,32(4):692-702.
[129]孙冬梅.手型和掌纹识别方法研究.北京交通大学博士论文.2003.
[130]S.Ribaric,and I.Fratric,A biometric identification system based on eigenpalm and eigenfinger features.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(11):1698-1709.
[131]K.Nandakumar.Integration of Multiple Cues in Biometric Systems.Marster dissertation.Michigan State University.2005.
[132]A.Ross,A.K.Jain,and J.Z.Qian.Information fusion in biometrics.Proceedings of the 3th International Conference on Audio and Video Based Person Authentication(AVBPA),2001.354-359.
[133]孙冬梅,裘正定.生物特征识别技术综述.电子学报,2001,29(12a):1744-1748.
[134]G.M.Lu,D.Zhang,and K.Wang.Pamprint recognition using Eigenpalms Features.Pattern Recognition Letters,2003,24(9):1463-1467.
[135]X.Wu,D.Zhang,and K.Wang,Fisherpalms bnased palmprint recognition,Pattern Recognition Letters,2003,24(15):2829-2838.
[136]O.Deniz.Castrillion,and Hernandez M,Face recognition using independent component analysis and support vector machines.Pattern Recognition Letters,2003,24(13):2153-2157.
[137]李文新.基于掌纹的在线身份验证.北京大学博士论文.2001.
[138]J.You,W.Li,and D.Zhang.Hierarchical palmprint identification via multiple feature extraction.Pattern Recognition,2002,35(4):.847-859.
[139]D.Zhang.Palmprint Authentication.Kluwer Acadenic 2004.
[140]戴青云,余英林,张大鹏.一种基于形态中值金字塔的线特征提取方法.华南理工大学学报(自然科学版.2001,29(5):14-18.
[141]N.Duta,and A.K.Jain.Mathing of palmprints.Pattern Recognition Letters,2002,23(4):477-485.
[142]李强.手部特征识别及特征级融合算法研究.北京交通大学博士学位论文.2006.
[143]M.M.Loeve.Probability Theory.Princeton,1955.
[144]L.Sirovich,and M.Kirby.Low dimentional procedure for the Characterization of human faces.Journal of the Optical Society of America,A,1987,4(3):519-524.
[145]M.Turk,and A.Pentland.Eigenfaces for recognition,Journal of Cognitive Neuralscience,1991, 3(1):71-86.
[146]M.Turk,and A.Pentland.Face recognition using eigenfaces.Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition,1991.586-591.
[147]J.Yang,D.Zhang,A.F.Frangi,J.Y.Yang,Two-dimentional PCA:a new approach to appearence-based face representation and recognition,IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(1):131-137.
[148]R.S.Reillo.Biometric identification through hand geometry measurements.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1168-1171.
[149]R.A.Fisher.The statistical utilization of multiple measurements.Annals of Eugenics.1938,8:376-386.
[150]R.A.Fisher.The use of multiple measures in taxonomic problems.Annals of Eugenics.1936,7:179-188.
[151]李铭.自动人脸检测与识别系统中若干问题的研究.北京交通大学博士学位论文.2005.
[152]D.L.Swets,and J.Weng.Using discriminant eigenfeatures for image retrieval.IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):831-836.
[153]A.M.Martinez,and A.C.Kak.PCA versus LDA.IEEE Transactions on Pattern Analysis and Machine Intelligence.2001,23(2):223-228.
[154]P.N.Belhumeur,J.P.Hespanha,and D.J.Kriegrnan.Eigenface vs.Fisherface:recognition using class specific linear projection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[155]R.Auckenthaler,M.Carey,and H.Lloyd-Tomas.Score normalization for text-independent speaker verification systems.Digital Signal Process,2000,10(1):42-54.
[156]F.A.Julian,O.G.Javier,and G.R.Joaquin.Target dependent score normalization techniques and their application to signature verification.IEEE Transactions on Systems,Man,and Cybernetics,Part C,2005,32(3):418-425.
[157]D.Reynolds.Comparison of background normalization methods for text-independent speaker verification.Eurospeech,1997.963-966.
[158]刘明,袁保宗,苗振江.一种新的双目标排序层分类器融合方法.自动化学报,2007,33(12):1276-1282.
[159]Image Processing Toolbox User's Guide,The Math Works Inc.,2001.
[160]C.C.Han,H.Chen,C.Lin,and K.Fan.Person authentification using Palmprint features.Pattern Recognition,2003,32(2):371-381.
[161]D.Zhang,W.K.Kong,and J.You.Online Palmprint verification.IEEE Transactions on Pattern Analysis andMachine Intelligence.2003,25(9):1041-1050.
[162]Y.Freund and R.Schapire.Experiments with a new boosting algorithm.Proceedings of the 13th International Conference on Machine Learning,1996.149-56.
[163]D.S.Lee,S.N Srihari.A theory of classifier combination:the neural network approach.Proceedings of the Third International Conference on Document Analysis and Recognition,1995. 42-45.
[164]P.Muneesawang,and L.Guan,SVM-Based decision fusion model for detecting concepts in films,6th International Conference on Information,Communications,and Signal Processing,2007.1-5.
[165]A.Sharkey,and N.Sharkey.Combining diverse neural networks.The Knowledge Engineering Review.1997.
[166]P.Cunningham,and J.Carney Diversity versus quality in classification ensembles based on feature selection.European Conference on Machine Learning,2000.109-116.
[167]L.I.Kuncheva,C.Whitaker,C.Shipp,and R.Duin.Is independence good for combining classifiers.Proceedings of the 15th International Conference on Pattern Recognition,2000,2:168-171.
[168]Z.H.Zhou,Y.Yang,Ensembling local learners trough multimodal perturbation,IEEE Transactions on Systems,Man,and Cybernetics,Part B,2005,35(4):725-735.
[169]Z.H.Zhou,and M.Li.Semisupervised regression with co-training,Proceedings of the 19th International Joint Conference on Artificial Intelligence,2005.908-913.
[170]Z.H.Zhou,and M.Li.,Semisupervised regression with co-training style algorithms.IEEE Transactions on Knowledge and Data Engineering,2007,19(11):1479-1493
[171]A.Asuncion,D.J.Newman.UCI Machine Learning Repository [http://www.ics.uci.edu/～mlearn/MLRepository.html].Irvine,CA:University of California,School of Information and Computer Science.2007.
[172]L.Breiman.Arcing classifiers.The Annals of Statistics,1998,26(3):801-849.
[173]P.Brodatz.Textures:A Photographic Album for Artists and Designers.Dover Publications,1966.
[174]L.Breiman,J.H.Friedman,R.A.Olshen,and C.J.Stone.Classification and regression trees.Wadsworth,1984.
[175]Neural Network Group.The ELENA Project[ftp.dice.ucl.ac.be/pub/neural-ets/].2005
[176]A.K.Jain,K.Nandakumar,and A.Ross.Score normalization in multimodal biometric systems.Pattern Recognition,2005,38(12):2270 - 2285.
[177]朱辉,多分类器系统关键技术及应用研究,哈尔滨工业大学博士论文,2007.
[178]R.Hu,R.I.Damper,A 'no panacea theorem ' for classifier combination,Pattern Recognition,2008,41(8):2665-2673.