This book constitutes the refereed proceedings of the 15th International Conference on Algorithmic Learning Theory, ALT 2004, held in Padova, Italy in October 2004.
The 29 revised full papers presented together with 5 invited papers and 3 tutorial summaries were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on inductive inference, PAC learning and boosting, statistical supervised learning, online sequence learning, approximate optimization algorithms, logic based learning, and query and reinforcement learning.
Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and combinatorics. There is also considerable interaction with the practical, empirical fields of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena.
The papers in this volume cover a broad range of topics of current research in the field of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) reflecting this broad range. The categories featured are Inductive Inference, Approximate Optimization Algorithms, Online Sequence Prediction, Statistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical Supervised Learning, Logic Based Learning, and Query & Reinforcement Learning.
Below we give a brief overview of the field, placing each of these topics in the general context of the field. Formal models of automated learning reflect various facets of the wide range of activities that can be viewed as learning.
