Algorithmic Learning Theory

Naoki Abe

ISBN 10: 3540428755 / ISBN 13: 9783540428756

Published by Springer Berlin Heidelberg Nov 2001, 2001

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25-28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti cial Intelligence Vol. 2226). 400 pp. Englisch.

Seller Inventory # 9783540428756

Bibliographic Details

• Title: Algorithmic Learning Theory
• Publisher: Springer Berlin Heidelberg Nov 2001
• Publication Date: 2001
• Language: English
• ISBN 10: 3540428755
• ISBN 13: 9783540428756
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 22D millimeters 235 millimeters width by 155 millimeters height by 22 millimeters depth
• Weight: 604 grams

About this title

Synopsis

This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).

Synopsis

This book constitutes the refereed proceedings of the 12th International Conference on Algorithmic Learning Theory, ALT 2001, held in Washington, DC, USA in November 2001. The 21 revised full papers presented together with two invited papers and an introduction by the volume editors were carefully reviewed and selected from 42 submissions. The papers are organized in topical sections on complexity of learning, support vector machines, new learning models, online learning, inductive inference, refutable inductive inference, learning structures and languages.