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

Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.
Format/Description:
Book
1 online resource (CCCXLVIII, 338 pages)
Edition:
1st ed. 1997.
Series:
Computer Science (Springer-11645)
Lecture notes in computer science. Lecture notes in artificial intelligence 1208
Lecture Notes in Artificial Intelligence ; 1208
Contained In:
Springer eBooks
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Subjects:
Artificial intelligence.
Logic, Symbolic and mathematical.
Computers.
Local subjects:
Artificial Intelligence. (search)
Mathematical Logic and Formal Languages. (search)
Computation by Abstract Devices. (search)
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.
Contributor:
Ben-David, Shai, editor., Editor,
SpringerLink (Online service)
Other format:
Printed edition:
Printed edition:
ISBN:
978-3-540-68431-2
9783540684312
Publisher Number:
10.1007/3-540-62685-9 doi
Access Restriction:
Restricted for use by site license.