Algorithmic Learning Theory : 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings / edited by Ricard Gavaldà, Klaus P. Jantke, Eiji Takimoto.

Format:

Book

Contributor:

Gavaldà, Ricard, 1964- editor.

Jantke, K. P. (Klaus P.), editor.

Takimoto, Eiji, 1964- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2842.

Lecture Notes in Artificial Intelligence ; 2842

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Algorithms.

Logic, Symbolic and mathematical.

Natural language processing (Computer science).

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Natural Language Processing (NLP).

Local Subjects:

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Natural Language Processing (NLP).

Physical Description:

1 online resource (XII, 320 pages).

Edition:

First edition 2003.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.

System Details:

text file PDF

Contents:

Invited Papers

Abduction and the Dualization Problem

Signal Extraction and Knowledge Discovery Based on Statistical Modeling

Association Computation for Information Access

Efficient Data Representations That Preserve Information

Can Learning in the Limit Be Done Efficiently?

Inductive Inference

Intrinsic Complexity of Uniform Learning

On Ordinal VC-Dimension and Some Notions of Complexity

Learning of Erasing Primitive Formal Systems from Positive Examples

Changing the Inference Type - Keeping the Hypothesis Space

Learning and Information Extraction

Robust Inference of Relevant Attributes

Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables

Learning with Queries

On the Learnability of Erasing Pattern Languages in the Query Model

Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries

Learning with Non-linear Optimization

Kernel Trick Embedded Gaussian Mixture Model

Efficiently Learning the Metric with Side-Information

Learning Continuous Latent Variable Models with Bregman Divergences

A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation

Learning from Random Examples

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

Learning a Subclass of Regular Patterns in Polynomial Time

Identification with Probability One of Stochastic Deterministic Linear Languages

Online Prediction

Criterion of Calibration for Transductive Confidence Machine with Limited Feedback

Well-Calibrated Predictions from Online Compression Models

Transductive Confidence Machine Is Universal

On the Existence and Convergence of Computable Universal Priors.

Other Format:

Printed edition:

ISBN:

978-3-540-39624-6

9783540396246

Access Restriction:

Restricted for use by site license.