Computational Learning Theory and Natural Learning Systems, Volume 2

This second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. It seeks to define plausible areas of joint research. The contributions are concerned with finding constraints for theory, while at the same time interpreting theoretic results in the context of experiments with actual learning systems.

Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers), and those trying to analyze them.

The first section provides theoretical explanations for the learning systems addressed; the second section focuses on issues in model selection and inductive bias. The third section presents new learning algorithms, the fourth explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of algorithms.