Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives Produced as Part of the Esprit Working Group in Neural and Computational Learning, Neurocolt 8556

Perspectives of Current Research about the Complexity of Learning on Neural Nets Produced as Part of the Esprit Working Group in Neural and Computational Learning, Neurocolt 8556

Probabilistic Analysis of Learning in Artiicial Neural Networks: the Pac Model and Its Variants Produced as Part of the Esprit Working Group in Neural and Computational Learning, Neurocolt 8556

1 1 A version of this is to appear as a chapter in The Computational and Learning Complexity of Neural Networks (ed. Ian Parberry), MIT Press. 2 Abstract There are a number of mathematical approaches to the study of learning and generalization in artiicial neural networks. Here we survey thèprobably approximately correct' (PAC) model of learning and some of its variants. These models, much-stud...

Computing the Maximum Bichromatic Discrepancy, with Applications to Computer Graphics and Machine Learning Produced as Part of the Esprit Working Group in Neural and Computational Learning, Neurocolt 8556

Computing the maximum bichromatic discrepancy is an interesting theoretical problem with important applications in computational learning theory, computational geometry and computer graphics. In this paper we give algorithms to compute the maximum bichromatic discrepancy for simple geometric ranges, including rectangles and halfspaces. In addition, we give extensions to other discrepancy problems.

A General Feedforward Neural Network Model C Edric Gegout 123 Produced as Part of the Esprit Working Group in Neural and Computational Learning, Neurocolt 8556

In this paper, we generalize a model proposed by L eon Bottou and Patrick Gallinari in 3]. This model gives a general mathematical description of feedforward neural networks, for which standard models, such as Multi-Layer Perceptrons or Radial Basis Function based neural networks, are only particular cases. A generalized back-propagation, which gives an eecient way to compute the diierential of...

Techniques in Neural Learning Produced as Part of the Esprit Working Group in Neural and Computational Learning, Neurocolt 8556

This paper takes ideas developed in a theoretical framework by Maass 8] and adapts them for a practical learning algorithm for feedforward sigmoid neural networks. A number of diierent techniques are presented which are based loosely around the common theme of taking advantage of the lin-earity of the net input to a neuron, or in other words the fact that there is only a single non-linearity pe...