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Information Representation

  • Ovidiu CalinEmail author
Chapter
  • 39 Downloads
Part of the Springer Series in the Data Sciences book series (SSDS)

Abstract

This chapter deals with the information representation in neural networks and the description of the information content of several types of neurons and networks using the concept of sigma-algebra. The main idea is to describe the evolution of the information content through the layers of a network. The network’s input is considered to be a random variable, being characterized by a certain information. Consequently, all network layer activations will be random variables carrying forward some subset of the input information, which are described by some sigma-fields. From this point of view, neural networks can be interpreted as information processors.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mathematics & StatisticsEastern Michigan UniversityYpsilantiUSA

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