"Orthogonal Bases Are the Best: A Theorem Justifying Bruno Apolloni's H" by Jaime Nava and Vladik Kreinovich

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Departmental Technical Reports (CS)

Title

Orthogonal Bases Are the Best: A Theorem Justifying Bruno Apolloni's Heuristic Neural Network Idea

Authors

Jaime Nava, University of Texas at El PasoFollow
Vladik Kreinovich, University of Texas at El PasoFollow

Publication Date

6-2011

Comments

Technical Report: UTEP-CS-11-34

To appear in Journal of Uncertain Systems, 2012, Vol. 6, No. 2.

Abstract

One of the main problems with neural networks is that they are often very slow in learning the desired dependence. To speed up neural networks, Bruno Apolloni proposed to othogonalize neurons during training, i.e., to select neurons whose output functions are orthogonal to each other. In this paper, we use symmetries to provide a theoretical explanation for this heuristic idea.

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