Computational Learning Theory : Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings / edited by Shai Ben-David.

Format:

Book

Contributor:

Ben-David, Shai, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1208.

Lecture Notes in Artificial Intelligence ; 1208

Language:

English

Subjects (All):

Artificial intelligence.

Logic, Symbolic and mathematical.

Computers.

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Computation by Abstract Devices.

Local Subjects:

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Computation by Abstract Devices.

Physical Description:

1 online resource (CCCXLVIII, 338 pages).

Edition:

First edition 1997.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the Third European Conference on Computational Learning Theory, EuroCOLT'97, held in Jerusalem, Israel, in March 1997. The book presents 25 revised full papers carefully selected from a total of 36 high-quality submissions. The volume spans the whole spectrum of computational learning theory, with a certain emphasis on mathematical models of machine learning. Among the topics addressed are machine learning, neural nets, statistics, inductive inference, computational complexity, information theory, and theoretical physics.

Contents:

Sample compression, learnability, and the Vapnik-Chervonenkis dimension

Learning boxes in high dimension

Learning monotone term decision lists

Learning matrix functions over rings

Learning from incomplete boundary queries using split graphs and hypergraphs

Generalization of the PAC-model for learning with partial information

Monotonic and dual-monotonic probabilistic language learning of indexed families with high probability

Closedness properties in team learning of recursive functions

Structural measures for games and process control in the branch learning model

Learning under persistent drift

Randomized hypotheses and minimum disagreement hypotheses for learning with noise

Learning when to trust which experts

On learning branching programs and small depth circuits

Learning nearly monotone k-term DNF

Optimal attribute-efficient learning of disjunction, parity, and threshold functions

learning pattern languages using queries

On fast and simple algorithms for finding Maximal subarrays and applications in learning theory

A minimax lower bound for empirical quantizer design

Vapnik-Chervonenkis dimension of recurrent neural networks

Linear Algebraic proofs of VC-Dimension based inequalities

A result relating convex n-widths to covering numbers with some applications to neural networks

Confidence estimates of classification accuracy on new examples

Learning formulae from elementary facts

Control structures in hypothesis spaces: The influence on learning

Ordinal mind change complexity of language identification

Robust learning with infinite additional information.

Other Format:

Printed edition:

ISBN:

978-3-540-68431-2

9783540684312

Access Restriction:

Restricted for use by site license.