Kernel Combination Through Genetic Programming for Image Classification

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• 关键词：Genetic programming ; Support vector machines ; Kernel combination ; Image classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9423
• 期：1
• 页码：314-321
• 全文大小：282 KB
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• 作者单位：Yuri H. Ribeiro (15)
  Zenilton K. G. do Patrocínio Jr. (15)
  Silvio Jamil F. Guimarães (15)
  
  15. Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte, Brazil
  
• 丛书名：Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• ISBN：978-3-319-25751-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Support vector machine is a supervised learning technique which uses kernels to perform nonlinear separations of data. In this work, we propose a combination of kernels through genetic programming in which the individual fitness is obtained by a K-NN classifier using a kernel-based distance measure. Experiments have shown that our method KGP-K is much faster than other methods during training, but it is still able to generate individuals (i.e., kernels) with competitive performance (in terms of accuracy) to the ones that were produced by other methods. KGP-K produces reasonable kernels to use in the SVM with no knowledge about the distribution of data, even if they could be more complex than the ones generated by other methods and, therefore, they need more time during tests.