"Linear Neural Networks Revisited: From PageRank to Family Happiness" by Vladik Kreinovich

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Departmental Technical Reports (CS)

Title

Linear Neural Networks Revisited: From PageRank to Family Happiness

Authors

Vladik Kreinovich, University of Texas at El PasoFollow

Publication Date

8-2011

Comments

Technical Report: UTEP-CS-11-42

Abstract

The study of Artificial Neural Networks started with the analysis of linear neurons. It was then discovered that networks consisting only of linear neurons cannot describe non-linear phenomena. As a result, most currently used neural networks consist of non-linear neurons. In this paper, we show that in many cases, linear neurons can still be successfully applied. This idea is illustrated by two examples: the PageRank algorithm underlying the successful Google search engine and the analysis of family happiness.

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