Extended Stochastic Complexity and Minimax Relative Loss Analysis
Kenji Yamanishi (NEC, C&C Media Research Lab.)
Algebraic Analysis for Singular Statistical Estimation
Sumio Watanabe (Tokyo Inst. of Tech.)
Generalization Error of Linear Neural Networks in Unidentifiable
Cases
Kenji Fukumizu (RIKEN)
The Computational Limits to the Cognitive Power of the Neuroidal
Tabula Rasa
Jiri Wiedermann (AS of Czech Republic)
The Melting Pot of Automated Discovery: Principles for a New Science
Jan M. Zytkow (U. North Carolina).
The Consistency Dimension and Distribution-Dependent Learning
from Queries
Jose L. Balcazar, Jorge Castro, David Guijarro (UP. Catalunya),
and Hans-Ulrich Simon (U. Bochum)
The VC-dimension of Subclasses of Pattern Languages
Andrew Mitchell (U. New South Wales), Tobias Scheffer (U. Magdeburg),
Arun Sharma (U. New South Wales), and Frank Stephan (U. Heidelberg)
On the V_gamma Dimension for Regression in Reproducing Kernel
Hibert Spaces
Theodoros Evgeniou, Massimiliano Pontil (MIT)
On the Strength of Incremental Learning
Steffen Lange (U. Leipzig) and Gunter Grieser (TU. Darmstadt)
Learning from Random Text
Peter Rossmanith (TU. Muenchen)
Inductive Learning with Corroboration
Phil Watson (U. Kent at Canterbury)
Tailoring Representations to Different Requirements
Katharina Morik (U. Dortmund)
Flattening and Implication
Kouich Hirata (Kyushu Inst. of Tech.)
Induction of Logic Programs Based on Psi-Terms
Yutaka Sasaki (NTT, Comm. Sci. Lab.)
Complexity in the Case Against Accuracy: When Building One
Function-Free Horn Clause is as Hard as Any
Richard Nock (U. Antilles-Guyane)
A Method of Similarity-Driven Knowledge Revision for Type
Specification
Nobuhiro Morita, Makoto Haraguchi, and Yoshiaki Okubo (Hokkaido U.)
Boosting: Theory and Applications
Robert E. Schapire (AT&T Labs, Shannon Lab.)
PAC Learning with Nasty Noise
Nader H.Bshouty, Nadav Eiron, and Eyal Kushilevitz (Technion)
Positive and Unlabeled Examples Help Learning
Francesco De Comite, Francois Denis, Remi Gilleron,
and Fabien Letouzey (LIFL)
Learning Real Polynomials with a Turing Machine
Dennis Cheung (City U. Hong Kong)
Expressive Probability Models in Science
Stuart Russell (U. California at Berkeley)
Faster Near-Optimal Reinforcement Learning Adding Adaptiveness to
the E3 Algorithm
Carlos Domingo (Tokyo Inst. of Tech.)
A Note on Support Vector Machine Degeneracy
Ryan Rifkin, Massimiliano Pontil, and Alessandro Verri (MIT)
Learnability of Enumerable Classes of Recursive Functions from
"Typical" Examples
Jochen Nessel (U. Kaiserslautern)
On the Uniform Learnability of Approximations to Non-Recursive
Functions
Frank Stephan (U. Heidelberg) and Thomas Zeugmann (Kyushu U.)
Learning Minimal Covers of Functional Dependencies with Queries
Montserrat Hermo (UPV/EHU) and Vitor Lavin (U. Computense)
Boolean Formulas are Hard to Learn for Most Gate Basis
Victor Dalmau (UP. Catalunya)
Finding Relevant Variables in PAC Model with Membership Queries
David Guijarro (UP. Catalunya), Jun Tarui (U. Electro. Comm.),
and Tatsuie Tsukiji (Nagoya U.)
General Linear Relations among Different Types of Predictive
Complexity
Yuri Kalnishkan (U. London)
Predicting Nearly as Well as the Best Pruning of a Planar
Decision Graph
Eiji Takimoto (Tohoku U.) and Manfred K. Warmuth (U. California
at Santa Cruz)
On Learning Unions of Pattern Languages and Tree Patterns
Sally Goldman (Washington U.) and Stephen Kwek (Washington
State U.)