Table of Contents |
|
| Editors' Introduction | 1 - 7 | |
| Naoki Abe, Roni Khardon, and T. Zeugmann | ||
Invited Papers | ||
| The Discovery Science Project in Japan, Abstract. | 9 - 11 | |
|
Setsuo Arikawa
| ||
| Queries Revisited, Abstract. | 12 - 31 | |
|
Dana Angluin
| ||
| Robot Baby 2001, Abstract. | 32 - 56 | |
|
Paul R. Cohen,
Tim Oates,
Niall Adams, and
Carole R. Beal
| ||
|
Discovering Mechanisms: A Computational Philosophy of Science Perspective,
Abstract. | 57 - 57 | |
|
Lindley Darden
| ||
| Inventing Discovery Tools: Combining Information Visualization with Data Mining, Abstract. | 58 - 58 | |
|
Ben Shneiderman
| ||
Regular Contributions | ||
Complexity of Learning Learning | ||
|
On Learning Correlated Boolean Functions Using Statistical Queries
(Extended Abstract), Abstract. | 59 - 76 | |
|
Ke Yang
| ||
|
A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm,
Abstract. | 77 - 91 | |
|
Kohei Hatano
| ||
| Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard, Abstract. | 92 - 105 | |
|
Jiri Sima
| ||
Support Vector Machines | ||
| Learning of Boolean Functions Using Support Vector Machines, Abstract. | 106 - 118 | |
|
Ken Sadohara
| ||
|
A Random Sampling Technique for Training Support Vector Machines,
Abstract. | 119 - 134 | |
|
José Balcázar,
Yang Dai, and
Osamu Watanabe
| ||
New Learning Models | ||
| Learning Coherent Concepts Structured , Abstract. | 135 - 150 | |
| Ashutosh Garg, Dan Roth | ||
| Learning Intermediate Concepts, Abstract. | 151 - 166 | |
|
Stephen S. Kwek
| ||
| Real-Valued Multiple-Instance Learning with Queries, Abstract. | 167 - 180 | |
|
Daniel R. Dooly,
Sally A. Goldman
and Stephen S. Kwek
| ||
Online Learning | ||
| Loss Functions, Complexities, and the Legendre Transformation , Abstract. | 181 - 189 | |
|
Yuri Kalnishkan,
Michael V. Vyugin and
Volodya Vovk
| ||
|
Non-linear Inequalities between Predictive and Kolmogorov Complexities,
Abstract. | 190 - 204 | |
|
Michael V. Vyugin and
Vladimir V. V'yugin
| ||
Inductive Inference | ||
| Learning by Switching Type of Information, Abstract. | 205 - 218 | |
|
Sanjay Jain and
Frank Stephan
| ||
| Learning How to Separate , Abstract. | 219 - 234 | |
|
Sanjay Jain and
Frank Stephan
| ||
| Learning Languages in a Union , Abstract. | 235 - 250 | |
|
Sanjay Jain, Yen Kaow Ng, and Tiong Seng Tay
| ||
|
On the Comparison of Inductive Inference Criteria for Uniform Learning of
Finite Classes , Abstract. | 251 - 266 | |
|
Sandra Zilles
| ||
Refutable Inductive Inference | ||
| Refutable Language Learning with a Neighbor System , Abstract. | 267 - 282 | |
|
Yasuhito Mukouchi and Masako Sato
| ||
| Learning Recursive Functions Refutably , Abstract. | 283 - 298 | |
|
Sanjay Jain,
Efim Kinber,
Rolf Wiehagen and
Thomas Zeugmann
| ||
| Refuting Learning Revisited , Abstract. | 299 - 314 | |
|
Wolfgang Merkle and
Frank Stephan
| ||
Learning Structures and Languages | ||
| Efficient Learning of Semi-structured Data from Queries , Abstract. | 315 - 331 | |
|
Hiroki Arimura,
Hiroshi Sakamoto
and
Setsuo Arikawa
| ||
| Extending Elementary Formal Systems , Abstract. | 332 - 347 | |
|
Steffen Lange,
Gunter Grieser,
Klaus P. Jantke
| ||
| Learning Regular Languages Using RFSA , Abstract. | 348 - 363 | |
|
François Denis,
Aurélien Lemay and
Alain Terlutte
| ||
|
Inference of | 364 - 377 | |
|
Colin de la Higuera and
Jean-Christophe Janodet
| ||
| Author Index | 379 |