Energy functions for self-organizing maps

被引：108

作者：

Heskes, T ^{[1
]}

机构：

[1] Univ Nijmegen, RWCP, Theoret Fdn SNN, NL-6252 EZ Nijmegen, Netherlands

来源：

KOHONEN MAPS | 1999年

关键词：

D O I：

10.1016/B978-044450270-4/50024-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

By slightly changing the definition of the winning unit, Kohonen's original learning rule can be viewed as performing stochastic gradient descent on an energy function. We show this in two ways: by explicitely computing derivatives and as a limiting case of a "soft" version of self-organizing maps with probabilistic winner assignments. Kinks in a one-dimensional map and twists in a two-dimensional map correspond to local: minima in the energy landscape of the network weights.

引用

页码：303 / 315

页数：13

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