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2. EuroCOLT 1995: Barcelona, Spain
- Paul M. B. Vitányi:
Computational Learning Theory, Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13-15, 1995, Proceedings. Lecture Notes in Computer Science 904, Springer 1995, ISBN 3-540-59119-2
Session 1
- Ray J. Solomonoff:
The discovery of algorithmic probability: A guide for the programming of true creativity. 1-22 - Yoav Freund, Robert E. Schapire:
A decision-theoretic generalization of on-line learning and an application to boosting. 23-37 - Shai Ben-David, Eyal Kushilevitz, Yishay Mansour:
Online learning versus offline learning. 38-52
Session 2
- Shai Ben-David, Michael Lindenbaum:
Learning distributions by their density-levels - a paradigm for learning without a teacher. 53-68 - David Haussler, Jyrki Kivinen, Manfred K. Warmuth:
Tight worst-case loss bounds for predicting with expert advice. 69-83 - Kenji Yamanishi:
On-line maximum likelihood prediction with respect to general loss functions. 84-98
Session 3
- Andris Ambainis:
The power of procrastination in inductive inference: How it depends on used ordinal notations. 99-111 - José L. Balcázar, Harry Buhrman, Montserrat Hermo:
Learnability of Kolmogorov-easy circuit expressions via queries. 112-124 - Steffen Lange, Thomas Zeugmann:
Trading monotonicity demands versus mind changes. 125-139
Session 4
- John Case, Susanne Kaufmann, Efim B. Kinber, Martin Kummer:
Learning recursive functions from approximations. 140-153 - Rusins Freivalds, Efim B. Kinber, Carl H. Smith:
On the intrinsic complexity of learning. 154-168 - Sanjay Jain, Arun Sharma:
The structure of intrinsic complexity of learning. 169-181 - Rusins Freivalds, Sanjay Jain:
Kolmogorov numberings and minimal identification. 182-195
Session 5
- Jorma Rissanen:
Stochastic complexity in learning. 196-210 - Martin Anthony, Peter L. Bartlett:
Function learning from interpolation. 211-221 - Leonid Gurvits, Pascal Koiran:
Approximation and learning of convex superpositions. 222-236
Session 6
- V. G. Vovk:
Minimum description length estimators under the optimal coding scheme. 237-251 - Pekka Kilpeläinen, Heikki Mannila, Esko Ukkonen:
MDL learning of unions of simple pattern languages from positive examples. 252-260 - Eric Martin, Daniel N. Osherson:
A note on the use of probabilities by mechanical learners. 261-271
Session 7
- Dale Schuurmans:
Characterizing rational versus exponential learning curves. 272-286 - Marco Muselli:
Is Pocket algorithm optimal? 287-297 - Jan van den Berg, Jan C. Bioch:
Some theorems concerning the free energy of (un)constrained stochastic hopfield neural networks. 298-312
Session 8
- Foued Ameur:
A space-bounded learning algorithm for axis-parallel rectangles. 313-321 - Hans Ulrich Simon:
Learning decision lists and trees with equivalence-queries. 322-336
Session 9
- Marek Karpinski, Angus Macintyre:
Bounding VC-dimension of neural networks: Progress and prospects. 337-341 - Mostefa Golea:
Average case analysis of a learning algorithm for µ-DNF expressions. 342-356 - Eli Shamir, Clara Shwartzman:
Learning by extended statistical queries and its relation to PAC learning. 357-366
Session 10
- Takeshi Koshiba:
Typed pattern languages and their learnability. 367-379 - Francesco Bergadano, S. Varrichio:
Learning behaviors of automata from shortest counterexamples. 380-391 - Alvis Brazma:
Learning of regular expressions by pattern matching. 392-403 - Carlos Domingo, Víctor Lavín:
The query complexity of learning some subclasses of context-free grammars. 404-414
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