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ORCID3rd COLT 1990: Rochester, NY, USA
- Mark A. Fulk, John Case:
Proceedings of the Third Annual Workshop on Computational Learning Theory, COLT 1990, University of Rochester, Rochester, NY, USA, August 6-8, 1990. Morgan Kaufmann 1990, ISBN 1-55860-146-5 - Rusins Freivalds:
Inductive Inference of Minimal Programs. 3-22 - Thomas R. Hancock:
Identifying µ-Formula Decision Trees with Queries. 23-37 - Vijay Raghavan, Stephen R. Schach:
Learning Switch Configurations. 38-51 - Naoki Abe, Manfred K. Warmuth:
On the Computational Complexity of Approximating Distributions by Probabilistic Automata. 52-66 - Kenji Yamanishi:
A Learning Criterion for Stochastic Rules. 67-81 - Ker-I Ko:
On the Complexity of Learning Minimum Time-Bounded Turing Machines. 82-96 - Takeshi Shinohara:
Inductive Inference from Positive Data is Powerful. 97-110 - Keith Wright:
Inductive Identification of Pattern Languages Restricted Substitutions. 111-121 - Robert E. Schapire:
Pattern Languages are not Learnable. 122-129 - Paul Fischer, Hans Ulrich Simon:
On Learning Ring-Sum-Expansions. 130-143 - Avrim Blum, Mona Singh:
Learning Functions of k Terms. 144-153 - Bonnie Eisenberg, Ronald L. Rivest:
On the Sample Complexity of PAC-Learning Using Random and Chosen Examples. 154-162 - Sanjay Jain, Arun Sharma:
Finite Learning by a "Team". 163-177 - Efim B. Kinber:
Some Problems of Learning with an Oracle. 178-186 - Daniel N. Osherson, Michael Stob, Scott Weinstein:
A Mechanical Method of Successful Scientific Inquiry. 187-201 - Yoav Freund:
Boosting a Weak Learning Algorithm by Majority. 202-216 - Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
On the Sample Complexity of Weak Learning. 217-231 - Shai Ben-David, Alon Itai, Eyal Kushilevitz:
Learning by Distances. 232-245 - Martin Anthony, Norman Biggs, John Shawe-Taylor:
The Learnability of Formal Concepts. 246-257 - Eric B. Baum:
Polynomial Time Algorithms for Learning Neural Nets. 258-272 - Philip M. Long, Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability. 273-287 - David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth:
Learning Integer Lattices. 288-302 - Hans Ulrich Simon:
On the Number of Examples and Stages Needed for Learning Decision Trees. 303-313 - Karsten A. Verbeurgt:
Learning DNF Under the Uniform Distribution in Quasi-Polynomial Time. 314-326 - Efim B. Kinber, William I. Gasarch, Thomas Zeugmann, Mark G. Pleszkoch, Carl H. Smith:
Learning Via Queries With Teams and Anomilies. 327-337 - William I. Gasarch, Mark G. Pleszkoch, Robert Solovay:
Learning Via Queries in [+, <]. 338-351 - Pekka Orponen, Russell Greiner:
On the Sample Complexity of Finding Good Search Strategies. 352-358 - Javed A. Aslam, Ronald L. Rivest:
Inferring Graphs from Walks. 359-370 - V. G. Vovk:
Aggregating Strategies. 371-386 - Dana Angluin, Michael Frazier, Leonard Pitt:
Learning Conjunctions of Horn Clauses (Abstract). 387 - Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). 388 - Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). 389 - Ramamohan Paturi, Michael E. Saks:
On Threshold Circuits for Parity (Abstract). 390 - Wolfgang Maass, György Turán:
On the Complexity of Learning from Counterexamples and Membership Queries (abstract). 391 - Mark A. Fulk:
Robust Separations in Inductive Inference (Abstract). 392 - Avrim Blum:
Separating PAC and Mistake-Bound Learning Models Over the Boolean Domain (Abstract). 393
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