ALT 2003

24 papers

A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation Joel Ratsaby

Abduction and the Dualization Problem Thomas Eiter

Association Computation for Information Access Akihiko Takano

Can Learning in the Limit Be Done Efficiently? Thomas Zeugmann

Changing the Inference Type - Keeping the Hypothesis Space Frank J. Balbach

Criterion of Calibration for Transductive Confidence Machine with Limited Feedback Ilia Nouretdinov, Vladimir Vovk

Efficient Data Representations That Preserve Information Naftali Tishby

Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables Yusuke Suzuki, Takayoshi Shoudai, Satoshi Matsumoto, Tomoyuki Uchida, Tetsuhiro Miyahara

Efficiently Learning the Metric with Side-Information Tijl De Bie, Michinari Momma, Nello Cristianini

Identification with Probability One of Stochastic Deterministic Linear Languages Colin de la Higuera, José Oncina

Intrinsic Complexity of Uniform Learning Sandra Zilles

Kernel Trick Embedded Gaussian Mixture Model Jingdong Wang, Jianguo Lee, Changshui Zhang

Learning a Subclass of Regular Patterns in Polynomial Time John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann

Learning Continuous Latent Variable Models with Bregman Divergences Shaojun Wang, Dale Schuurmans

Learning of Erasing Primitive Formal Systems from Positive Examples Jin Uemura, Masako Sato

Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries Satoshi Matsumoto, Yusuke Suzuki, Takayoshi Shoudai, Tetsuhiro Miyahara, Tomoyuki Uchida

On Ordinal VC-Dimension and Some Notions of Complexity Eric Martin, Arun Sharma, Frank Stephan

On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays Jirí Síma

On the Existence and Convergence of Computable Universal Priors Marcus Hutter

On the Learnability of Erasing Pattern Languages in the Query Model Steffen Lange, Sandra Zilles

Robust Inference of Relevant Attributes Jan Arpe, Rüdiger Reischuk

Signal Extraction and Knowledge Discovery Based on Statistical Modeling Genshiro Kitagawa

Transductive Confidence Machine Is Universal Ilia Nouretdinov, Vladimir V. V'yugin, Alex Gammerman

Well-Calibrated Predictions from Online Compression Models Vladimir Vovk