ML Anthology

COLT 1990

36 papers

A Learning Criterion for Stochastic Rules Kenji Yamanishi
PDF
A Mechanical Method of Successful Scientific Inquiry Daniel N. Osherson, Michael Stob, Scott Weinstein
Aggregating Strategies V. G. Vovk
Boosting a Weak Learning Algorithm by Majority Yoav Freund
Composite Geometric Concepts and Polynomial Predictability Philip M. Long, Manfred K. Warmuth
Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract) Michael J. Kearns, Robert E. Schapire
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract) Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
Finite Learning by a "Team" Sanjay Jain, Arun Sharma
Identifying Μ-Formula Decision Trees with Queries Thomas R. Hancock
Inductive Identification of Pattern Languages Restricted Substitutions Keith Wright
Inductive Inference from Positive Data Is Powerful Takeshi Shinohara
PDF
Inductive Inference of Minimal Programs Rusins Freivalds
Inferring Graphs from Walks Javed A. Aslam, Ronald L. Rivest
PDF
Learning by Distances Shai Ben-David, Alon Itai, Eyal Kushilevitz
Learning Conjunctions of Horn Clauses (Abstract) Dana Angluin, Michael Frazier, Leonard Pitt
Learning DNF Under the Uniform Distribution in Quasi-Polynomial Time Karsten A. Verbeurgt
Learning Functions of K Terms Avrim Blum, Mona Singh
Learning Integer Lattices David P. Helmbold, Robert Sloan, Manfred K. Warmuth
Learning Switch Configurations Vijay Raghavan, Stephen R. Schach
Learning via Queries in [+, <] William I. Gasarch, Mark G. Pleszkoch, Robert Solovay
Learning via Queries with Teams and Anomilies Efim B. Kinber, William I. Gasarch, Thomas Zeugmann, Mark G. Pleszkoch, Carl H. Smith
On Learning Ring-Sum-Expansions Paul Fischer, Hans Ulrich Simon
On the Complexity of Learning from Counterexamples and Membership Queries (abstract) Wolfgang Maass, György Turán
On the Complexity of Learning Minimum Time-Bounded Turing Machines Ker-I Ko
PDF
On the Computational Complexity of Approximating Distributions by Probabilistic Automata Naoki Abe, Manfred K. Warmuth
PDF
On the Number of Examples and Stages Needed for Learning Decision Trees Hans Ulrich Simon
On the Sample Complexity of Finding Good Search Strategies Pekka Orponen, Russell Greiner
On the Sample Complexity of PAC-Learning Using Random and Chosen Examples Bonnie Eisenberg, Ronald L. Rivest
On the Sample Complexity of Weak Learning Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
On Threshold Circuits for Parity (Abstract) Ramamohan Paturi, Michael E. Saks
Pattern Languages Are Not Learnable Robert E. Schapire
Polynomial Time Algorithms for Learning Neural Nets Eric B. Baum
Robust Separations in Inductive Inference (Abstract) Mark A. Fulk
Separating PAC and Mistake-Bound Learning Models over the Boolean Domain (Abstract) Avrim Blum
Some Problems of Learning with an Oracle Efim B. Kinber
The Learnability of Formal Concepts Martin Anthony, Norman Biggs, John Shawe-Taylor