Computational Learning Theory : Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings. 1st ed. 1995

種類:

電子ブック

責任表示:

edited by Paul Vitanyi

出版情報:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1995

著者名:

• Vitanyi, Paul.
• SpringerLink (Online service)

シリーズ名:

Lecture Notes in Artificial Intelligence ; 904

ISBN:

9783540491958 [3540491953]    

注記:

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 superposi
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.

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