DocumentCode :
817224
Title :
Kernel Discriminant Analysis Using Case-Specific Smoothing Parameters
Author :
Ghosh, A.K.
Author_Institution :
Theor. Stat. & Math. Unit, Indian Stat. Inst., Kotkata
Volume :
38
Issue :
5
fYear :
2008
Firstpage :
1413
Lastpage :
1418
Abstract :
In kernel discriminant analysis, one common practice is to use a fixed level of smoothing (estimated from training data) for classifying all unlabeled observations. But, in classification, a good choice of smoothing parameters also depends on the observation to be classified. Therefore, instead of using a fixed level of smoothing over the entire measurement space, it may be more useful to estimate the smoothing parameters depending on that specific observation. Here, we propose a simple method for this case-specific smoothing. Some benchmark data sets are analyzed to illustrate the performance of the proposed method.
Keywords :
learning (artificial intelligence); smoothing methods; benchmark data sets; case-specific smoothing parameters; kernel discriminant analysis; Bandwidth; Bayes risk; bootstrap; cross validation; kernel smoothing; misclassification rate; nearest neighbor; p-value; Algorithms; Artificial Intelligence; Discriminant Analysis; Pattern Recognition, Automated; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2008.925754
Filename :
4579254
Link To Document :
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