Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science)

Ben David, Shai [Editor]; Case, John [Editor]; Maruoka, Akira [Editor];

ISBN 10: 3540233563 / ISBN 13: 9783540233565

Published by Springer, 2004

About this Item

Ex-library paperback in very nice condition with the usual markings and attachments. Except for library markings, interior clean and unmarked. Tight binding.

Seller Inventory # UTD1422823

Bibliographic Details

• Title: Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science)
• Publisher: Springer
• Publication Date: 2004
• Language: English
• ISBN 10: 3540233563
• ISBN 13: 9783540233565
• Binding: Paperback
• Condition: Very Good

About this title

Synopsis

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 c- binatorics. There is also considerable interaction with the practical, empirical ?elds 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 ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.