Presentation 2002/10/10
Sequential Pattern Learning based on Test Feature Classifier
Yukinobu SAKATA, Shun'ichi KANEKO, Yuji TAKAGI, Hirohito OKUDA,
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Abstract(in English) Fundamental characteristics of sequential learning by Test Feature Classifier (TFC), which is non-parametric and effective even for small data, are examined. In the learning, after recognition of a set of unknown objects, they are feeded into the classifier in order to obtain a modified clasifier. We propose an efficient algorithm for reconstruction of prime tests, which are irreducible combinations of features capable to discriminate training patterns into correct classes, is formalized in cases of addition and removal of training patterns. Some strategies for the modification of the training set are investigated with respect to their precision and performance by use of real pattern data.
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Keyword(in English) classification / test feature classifier / sequential learning / innovation strategy
Paper # NC2002-48
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Committee NC
Conference Date 2002/10/10(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sequential Pattern Learning based on Test Feature Classifier
Sub Title (in English)
Keyword(1) classification
Keyword(2) test feature classifier
Keyword(3) sequential learning
Keyword(4) innovation strategy
1st Author's Name Yukinobu SAKATA
1st Author's Affiliation Graduate School of Engineering, Hokkaido University()
2nd Author's Name Shun'ichi KANEKO
2nd Author's Affiliation Graduate School of Engineering, Hokkaido University
3rd Author's Name Yuji TAKAGI
3rd Author's Affiliation PERL, Hitachi Ltd
4th Author's Name Hirohito OKUDA
4th Author's Affiliation PERL, Hitachi Ltd
Date 2002/10/10
Paper # NC2002-48
Volume (vol) vol.102
Number (no) 381
Page pp.pp.-
#Pages 6
Date of Issue