Algorithmic Learning Theory : 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings / edited by Shai Ben David, John Case, Akira Maruoka.

Format:

Book

Contributor:

Ben-David, Shai, editor.

Case, John, 1942- editor.

Maruoka, Akira, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 3244.

Lecture Notes in Artificial Intelligence ; 3244

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 (XIV, 514 pages).

Edition:

First edition 2004.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.

System Details:

text file PDF

Summary:

Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning and Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.

Contents:

Invited Papers

String Pattern Discovery

Applications of Regularized Least Squares to Classification Problems

Probabilistic Inductive Logic Programming

Hidden Markov Modelling Techniques for Haplotype Analysis

Learning, Logic, and Probability: A Unified View

Regular Contributions

Learning Languages from Positive Data and Negative Counterexamples

Inductive Inference of Term Rewriting Systems from Positive Data

On the Data Consumption Benefits of Accepting Increased Uncertainty

Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space

Learning r-of-k Functions by Boosting

Boosting Based on Divide and Merge

Learning Boolean Functions in AC 0 on Attribute and Classification Noise

Decision Trees: More Theoretical Justification for Practical Algorithms

Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data

Complexity of Pattern Classes and Lipschitz Property

On Kernels, Margins, and Low-Dimensional Mappings

Estimation of the Data Region Using Extreme-Value Distributions

Maximum Entropy Principle in Non-ordered Setting

Universal Convergence of Semimeasures on Individual Random Sequences

A Criterion for the Existence of Predictive Complexity for Binary Games

Full Information Game with Gains and Losses

Prediction with Expert Advice by Following the Perturbed Leader for General Weights

On the Convergence Speed of MDL Predictions for Bernoulli Sequences

Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm

On the Complexity of Working Set Selection

Convergence of a Generalized Gradient Selection Approach for the Decomposition Method

Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions

Learnability of Relatively Quantified Generalized Formulas

Learning Languages Generated by Elementary Formal Systems and Its Application to SH Languages

New Revision Algorithms

The Subsumption Lattice and Query Learning

Learning of Ordered Tree Languages with Height-Bounded Variables Using Queries

Learning Tree Languages from Positive Examples and Membership Queries

Learning Content Sequencing in an Educational Environment According to Student Needs

Tutorial Papers

Statistical Learning in Digital Wireless Communications

A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networks

Approximate Inference in Probabilistic Models.

Other Format:

Printed edition:

ISBN:

978-3-540-30215-5

9783540302155

Access Restriction:

Restricted for use by site license.