Presentation | 1998/6/18 A Formulation of Learning Vector Quantization and an Analysis of Convergence Atsushi SATO, Keiji YAMADA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We have proposed a formulation of Learning Vector Quantization (LVQ) based on Minimum Classification Error (MCE) using a new misclassification measure. In this report, we attempt to clarify the convergence property of reference vectors mathematically from a dynamical point of view. We discuss the equilibrium states in a dynamical system for two-class classification, and prove that equilibrium points exist in the proposed formulation, while they do not exist in LVQ2.1 or the conventional formulation based on MCE. Simulation experiments for artificial data reveal that reference vectors are well converged in the proposed formulation. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Minimum Classification Error / Learning Vector Quantization / Dynamical System / Equilibrium State / Analysis of Convergence |
Paper # | PRMU98-35 |
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Committee | PRMU |
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Conference Date | 1998/6/18(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Formulation of Learning Vector Quantization and an Analysis of Convergence |
Sub Title (in English) | |
Keyword(1) | Minimum Classification Error |
Keyword(2) | Learning Vector Quantization |
Keyword(3) | Dynamical System |
Keyword(4) | Equilibrium State |
Keyword(5) | Analysis of Convergence |
1st Author's Name | Atsushi SATO |
1st Author's Affiliation | C&C Media Research Labs., NEC Corp.() |
2nd Author's Name | Keiji YAMADA |
2nd Author's Affiliation | C&C Media Research Labs., NEC Corp. |
Date | 1998/6/18 |
Paper # | PRMU98-35 |
Volume (vol) | vol.98 |
Number (no) | 126 |
Page | pp.pp.- |
#Pages | 8 |
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