
Computational Learning Theory
Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings
Paul Vitanyi(Editor)
Springer (Publisher)
Published on 23. February 1995
Book
Paperback/Softback
XVIII, 422 pages
978-3-540-59119-1 (ISBN)
Description
This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995.
The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.
The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.
More details
Series
Edition
1995 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XVIII, 422 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 24 mm
Weight
663 gr
ISBN-13
978-3-540-59119-1 (9783540591191)
DOI
10.1007/3-540-59119-2
Schweitzer Classification
Content
The discovery of algorithmic probability: A guide for the programming of true creativity.- A desicion-theoretic generalization of on-line learning and an application to boosting.- Online learning versus offline learning.- Learning distributions by their density levels - A paradigm for learning without a teacher.- Tight worst-case loss bounds for predicting with expert advice.- On-line maximum likelihood prediction with respect to general loss functions.- The power of procrastination in inductive inference: How it depends on used ordinal notations.- Learnability of Kolmogorov-easy circuit expressions via queries.- Trading monotonicity demands versus mind changes.- Learning recursive functions from approximations.- On the intrinsic complexity of learning.- The structure of intrinsic complexity of learning.- Kolmogorov numberings and minimal identification.- Stochastic complexity in learning.- Function learning from interpolation (extended abstract).- Approximation and learning of convex superpositions.- Minimum description length estimators under the optimal coding scheme.- MDL learning of unions of simple pattern languages from positive examples.- A note on the use of probabilities by mechanical learners.- Characterizing rational versus exponential learning curves.- Is pocket algorithm optimal?.- Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks.- A space-bounded learning algorithm for axis-parallel rectangles.- Learning decision lists and trees with equivalence-queries.- Bounding VC-dimension for neural networks: Progress and prospects.- Average case analysis of a learning algorithm for ?-DNF expressions.- Learning by extended statistical queries and its relation to PAC learning.- Typed pattern languages and theirlearnability.- Learning behaviors of automata from shortest counterexamples.- Learning of regular expressions by pattern matching.- The query complexity of learning some subclasses of context-free grammars.