Presentation 2013-03-14
Learning Process Visualization of LVQ and Learning Observation at Place where Turning Point Exists
Yoshiaki KUROSAWA,
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Abstract(in English) LVQ is one of the basic learning methods in the field of pattern recognition and it can be described by probabilistic descent method. From this aspect , it becomes one of steepest descent method applications. By this concept, the movement of recognition parameters, which are all the reference vector values of LVQ, is observable during learning. This observation technique was applied to the case of a handwritten numerical character recognition experiment. The case reported here was happened to be found in the experiment, and it was that the error rates obtained from almost same learning strength became largely different. As a result of the observation, a turning point was found where the two almost same learning processes were divided and introduced to two different learning processes which had largely different results.
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Keyword(in English) LVQ / probabilistic descent / steepest descent / learning / OCR / visualization / turning point
Paper # PRMU2012-198
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Conference Information
Committee PRMU
Conference Date 2013/3/7(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Learning Process Visualization of LVQ and Learning Observation at Place where Turning Point Exists
Sub Title (in English)
Keyword(1) LVQ
Keyword(2) probabilistic descent
Keyword(3) steepest descent
Keyword(4) learning
Keyword(5) OCR
Keyword(6) visualization
Keyword(7) turning point
1st Author's Name Yoshiaki KUROSAWA
1st Author's Affiliation Toshiba Solutions Corporation Fuchu Engineering Center()
Date 2013-03-14
Paper # PRMU2012-198
Volume (vol) vol.112
Number (no) 495
Page pp.pp.-
#Pages 6
Date of Issue