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Neural Networks and Computational Learning Theory

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NeuroCOLT Technical Report NC-TR-95-003

Perspectives of Current Research
about the Complexity of Learning on Neural Nets

Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz, Klosterwiesgasse 32/2,
A-8010 Graz
Austria

Abstract
This paper discusses within the framework of computational learning theory the current state of knowledge and some open problems in three areas of research about learning on feedforward neural nets:

• Neural nets that learn from mistakes

• Bounds for the Vapnik-Chervonenkis dimension of neural nets

• Agnostic PAC-learning of functions on neural nets.

All relevant definitions are given in this paper, and no previous knowledge about computational learning theory or neural nets is required.

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