Tailoring Representations to Different Requirements,
Abstract.
| | 1 - 12
|
|
Katharina Morik
| |
|
Theoretical Views of Boosting and Applications,
Abstract.
| | 13 - 25
|
|
Robert E. Schapire
| |
|
Extended Stochastic Complexity and Minimax Relative Loss Analysis,
Abstract.
| | 26 - 38
|
|
Kenji Yamanishi
|
|
| |
|
| |
|
REGULAR CONTRIBUTIONS
| |
|
| |
|
|
|
|
| |
|
| |
|
Neural Networks
| |
|
| |
|
Algebraic Analysis for Singular Statistical Estimation,
Abstract.
| | 39 - 50
|
|
Sumio Watanabe
| |
|
Generalization Error of Linear Neural Networks in Unidentifiable Cases,
Abstract.
| | 51 - 62
|
|
Kenji Fukumizu
| |
|
The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa,
Abstract.
| | 63 - 76
|
|
Jirí Wiedermann
|
|
| |
|
| |
|
Learning Dimension
| |
|
| |
|
The Consistency Dimension and Distribution-Dependent Learning from Queries,
Abstract.
| | 77 - 92
|
|
José L. Balcázar, Jorge Castro,
David Guijarro, and
Hans-Ulrich Simon
| |
|
The VC-Dimension of Subclasses of Pattern Languages,
Abstract.
| | 93 - 105
|
|
Andrew Mitchell,
Tobias Scheffer,
Arun Sharma, and
Frank Stephan
| |
|
On the Vγ Dimension for Regression in Reproducing Kernel Hilbert Spaces,
Abstract.
| | 106 - 117
|
|
Theodoros Evgeniou and Massimiliano Pontil
|
|
| |
|
| |
|
Inductive Inference
| |
|
| |
|
On the Strength of Incremental Learning,
Abstract.
| | 118 - 131
|
|
Steffen Lange and
Gunter Grieser
| |
|
Learning from Random Text,
Abstract.
| | 132 - 144
|
|
Peter Rossmanith
| |
|
Inductive Learning with Corroboration,
Abstract.
| | 145 - 156
|
|
Phil Watson
|
|
| |
|
| |
|
Inductive Logic Programming
| |
|
| |
|
Flattening and Implication,
Abstract.
| | 157 - 168
|
|
Kouichi Hirata
| |
|
Induction of Logic Programs Based on ψ-Terms,
Abstract.
| | 169 - 181
|
|
Yutaka Sasaki
| |
|
Complexity in the Case Against Accuracy: When Building One Function-Free
Horn Clause is as Hard as Any,
Abstract.
| | 182 - 193
|
|
Richard Nock
| |
|
A Method of Similarity-Driven Knowledge Revision for Type Specification,
Abstract.
| | 194 - 205
|
|
Nobuhiro Morita,
Makoto Haraguchi, and Yoshiaki Okubo
|
|
| |
|
| |
|
PAC Learning
| |
|
| |
|
PAC Learning with Nasty Noise,
Abstract. | | 206 - 218
|
|
Nader H. Bshouty,
Nadav Eiron, and
Eyal Kushilevitz
| |
|
Positive and Unlabeled Examples Help Learning,
Abstract.
| | 219 - 230
|
|
Francesco De Comité,
François Denis,
Rémi Gilleron,
and Fabien Letouzey
| |
|
Learning Real Polynomials with a Turing Machine,
Abstract.
| | 231 - 240
|
|
Dennis Cheung
|
|
| |
|
| |
|
Mathematical Tools for Learning
| |
|
| |
|
Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness
to the E3 Algorithm,
Abstract.
| | 241 - 251
|
|
Carlos Domingo
| |
|
A Note on Support Vector Machine Degeneracy,
Abstract.
| | 252 - 263
|
|
Ryan Rifkin, Massimiliano Pontil, and Alessandro Verri
|
|
| |
|
| |
|
Learning Recursive Functions
| |
|
| |
|
Learnability of Enumerable Classes of Recursive Functions from
“Typical” Examples,
Abstract.
| | 264 - 275
|
|
Jochen Nessel
| |
|
On the Uniform Learnability of Approximations to Non-Recursive Functions,
Abstract.
| | 276 - 290
|
|
Frank Stephan
and
Thomas Zeugmann
|
|
| |
|
| |
|
Query Learning
| |
|
| |
|
Learning Minimal Covers of Functional Dependencies with Queries,
Abstract.
| | 291 - 300
|
|
Montserrat Hermo and Víctor Lavín
| |
|
Boolean Formulas are Hard to Learn for Most Gate Bases,
Abstract.
| | 301 - 312
|
|
Victor Dalmau
| |
|
Finding Relevant Variables in PAC Model with Membership Queries,
Abstract.
| | 313 - 322
|
|
David Guijarro,
Jun Tarui, and Tatsuie Tsukiji
|
|
| |
|
| |
|
On-Line Learning
| |
|
| |
|
General Linear Relations among Different Types of Predictive Complexity,
Abstract.
| | 323 - 334
|
|
Yuri Kalnishkan
| |
|
Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph,
Abstract.
| | 335 - 346
|
|
Eiji Takimoto and
Manfred K. Warmuth
| |
|
On Learning Unions of Pattern Languages and Tree Patterns,
Abstract.
| | 347 - 363
|
|
Sally A. Goldman and
Stephen S. Kwek
| |
|
|
Author Index
| | 365
|
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