Algorithmic Learning Theory

12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001. Proceedings.

By: Naoki Abe (Editor), Roni Khardon (Editor), Thomas Zeugmann (Editor)

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Paperback | 7 November 2001

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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).

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Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Computer Science Mathematical Theory of Computation Medicine Medicine in General Artificial Intelligence Machine Learning Mathematics Mathematical Foundation

Mathematical Logic Graphical & Digital Media Applications Desktop Publishing Audio Processing

EditorsÆ Introduction	p. 1
Invited Papers
The Discovery Science Project in Japan	p. 9
Queries Revisited	p. 12
Robot Baby 2001	p. 32
Discovering Mechanisms: A Computational Philosophy of Science Perspective	p. 57
Inventing Discovery Tools: Combining Information Visualization with Data Mining	p. 58
Complexity of Learning
On Learning Correlated Boolean Functions Using Statistical Queries	p. 59
A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm	p. 77
Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard	p. 92
Support Vector Machines
Learning of Boolean Functions Using Support Vector Machines	p. 106
A Random Sampling Technique for Training Support Vector Machines (For Primal-Form Maximal-Margin Classifiers)	p. 119
New Learning Models
Learning Coherent Concepts	p. 135
Learning Intermediate Concepts	p. 151
Real-Valued Multiple-Instance Learning with Queries	p. 167
Online Learning
Loss Functions, Complexities, and the Legendre Transformation	p. 181
Non-linear Inequalities between Predictive and Kolmogorov Complexities	p. 190
Inductive Inference
Learning by Switching Type of Information	p. 205
Learning How to Separate	p. 219
Learning Languages in a Union	p. 235
On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes	p. 251
Refutable Inductive Inference
Refutable Language Learning with a Neighbor System	p. 267
Learning Recursive Functions Refutably	p. 283
Refuting Learning Revisited	p. 299
Learning Structures and Languages
Efficient Learning of Semi-structured Data from Queries	p. 315
Extending Elementary Formal Systems	p. 332
Learning Regular Languages Using RFSA	p. 348
Inference of ?-Languages from Prefixes	p. 364
Author Index	p. 379
Table of Contents provided by Publisher. All Rights Reserved.

Algorithmic Learning Theory

12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001. Proceedings.

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