Abstract
A mathematical theory of learning is presented in a unified manner to be applicable to various architectures of networks. The theory is based on parameter modification driven by a time series of input signals generated from a stochastic information source. A network modifies its behavior such that it adapts to the environmental information structure. The theory is self-organization of a neural system. A typical discrete structure is automatically formed through continuous parameter modification by self-organization.
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