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CNKI期刊论文0611(164)
标准(3)
在“
Elsevier电子期刊
”中,
命中:
18,746
条,耗时:0.1919075 秒
在所有数据库中总计命中:
332,238
条
1.
Comparing the use of
training
data
derived from legacy soil pits and soil survey polygons for mapping soil classes
作者:
Brandon Heung
a
;
brandon_heung@sfu.ca
;
Matú
;
&scaron
;
Hodú
;
l
b
;
mhodul@sfu.ca
;
Margaret G. Schmidt
a
;
margaret_schmidt@sfu.ca
关键词:
Digital soil mapping
;
Machine-learning
;
Soil classification
;
Data
-mining
;
Model comparison
;
Ensemble-learning
刊名:Geoderma
出版年:2017
2.
A novel spectral-spatial co-
training
algorithm for the transductive classification of hyperspectral imagery
data
关键词:
Hyperspectral imagery classification
;
Transductive learning
;
Collective inference
;
Co-
training
;
Spectral-spatial
data
刊名:Pattern Recognition
出版年:2017
3.
Large cost-sensitive margin distribution machine for imbalanced
data
classification
作者:
Fanyong Cheng
a
;
b
;
b12090031@hnu.edu.cn
Author Vitae
;
Jing Zhang
a
Author Vitae
;
Cuihong Wen
a
Author Vitae
;
Zhaohua Liu
c
Author Vitae
;
Zuoyong Li
b
Author Vitae
关键词:
Margin distribution
;
Cost-sensitive learning
;
Imbalanced
training
data
;
Balanced detection rate
刊名:Neurocomputing
出版年:2017
4.
Summit-
Training
: A hybrid Semi-Supervised technique and its application to classification tasks
作者:
L. Tencer
;
lukas.tencer@gmail.com
;
;
M. Reznakova
marta.reznakova@gmail.com
;
M. Cheriet
mohamed.cheriet@etsmtl.ca
关键词:
Hybrid methods
;
Semi-supervised learning
;
Classification
;
Summit-
Training
;
Query-by-Committee
;
Unlabeled
data
刊名:Applied Soft Computing
出版年:2017
5.
The US etonogestrel implant mandatory clinical
training
and active monitoring programs: 6-year experience
作者:
Mitchell D. Creinin
a
;
mdcreinin@ucdavis.edu
;
Andrew M. Kaunitz
b
;
Philip D. Darney
c
;
Lisa Schwartz
d
;
Tonja Hampton
d
;
Keith Gordon
d
;
Hans Rekers
e
关键词:
Contraceptive implant
;
Etonogestrel
;
Training
;
Monitoring
刊名:Contraception
出版年:2017
6.
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big
data
作者:
Jesus Maillo
;
a
;
jesusmh@decsai.ugr.es
;
Sergio Ramí
;
rez
a
;
sramirez@decsai.ugr.es
;
Isaac Triguero
c
;
d
;
e
;
Isaac.Triguero@nottingham.ac.uk
;
Francisco Herrera
a
;
b
;
herrera@decsai.ugr.es
关键词:
K-nearest neighbors
;
Big
data
;
Apache Hadoop
;
Apache Spark
;
MapReduce
刊名:Knowledge-Based Systems
出版年:2017
7.
The study of under- and over-sampling methods’ utility in analysis of highly imbalanced
data
on osteoporosis
作者:
M. Bach
a
;
malgorzata.bach@polsl.pl
;
;
A. Werner
;
a
;
aleksandra.werner@polsl.pl
;
;
J. Żywiec
b
;
jzywiec@sum.edu.pl
;
;
W. Pluskiewicz
c
;
osteolesna@poczta.onet.pl
;
关键词:
Classification
;
Imbalanced
data
;
Osteoporosis
;
Performance measures
;
Sampling methods
刊名:Information Sciences
出版年:2017
8.
Image set classification based on synthetic examples and reverse
training
作者:
Lin Zhang
a
;
b
;
cslinzhang@tongji.edu.cn
Author Vitae
;
Qingjun Liang
a
Author Vitae
;
Ying Shen
a
Author Vitae
;
Meng Yang
c
Author Vitae
;
Feng Liu
c
Author Vitae
关键词:
Face recognition
;
Image set classification
;
Reverse
training
刊名:Neurocomputing
出版年:2017
9.
Dynamic
training
protocol improves the robustness of PR-based myoelectric control
作者:
Dapeng Yang
a
;
Yikun Gu
a
;
Li Jiang
a
;
jiangli01@hit.edu.cn" class="auth_mail" title="E-mail the corresponding author
;
Luke Osborn
b
;
Hong Liu
a
关键词:
Transradial prosthesis
;
Surface electromyography (sEMG)
;
Myoelectric control
;
Training
protocol
;
Pattern recognition
刊名:Biomedical Signal Processing and Control
出版年:2017
10.
A novel
data
preprocessing method for boosting neural network performance: A case study in osteoporosis prediction
作者:
Theodoros Iliou
a
;
th.iliou@ct.aegean.gr
;
Christos-Nikolaos Anagnostopoulos
a
;
canag@ct.aegean.gr
;
Ioannis M. Stephanakis
b
;
stephan@ote.gr
;
George Anastassopoulos
c
;
anasta@med.duth.gr
关键词:
Data
pre-processing
;
Neural networks
;
Machine learning
;
Osteoporosis prediction
刊名:Information Sciences
出版年:2017
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