COLT 1995

56 papers

A Comparison of New and Old Algorithms for a Mixture Estimation Problem David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth

A Game of Prediction with Expert Advice V. G. Vovk

A Note on Learning Multivariate Polynomials Under the Uniform Distribution (Extended Abstract) Nader H. Bshouty

A Note on VC-Dimension and Measures of Sets of Reals Shai Ben-David, Leonid Gurvits

An Experimental and Theoretical Comparison of Model Selection Methods Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron

Being Taught Can Be Faster than Asking Questions Ronald L. Rivest, Yiqun Lisa Yin

Concept Learning with Geometric Hypotheses David P. Dobkin, Dimitrios Gunopulos

Corrigendum for "Learnability of Description Logics" William W. Cohen, Haym Hirsh

Criteria for Specifying Machine Complexity in Learning Changfeng Wang, Santosh S. Venkatesh

DNF - If You Can't Learn'em, Teach'em: An Interactive Model of Teaching H. David Mathias

Exactly Learning Automata with Small Cover Time Dana Ron, Ronitt Rubinfeld

From Noise-Free to Noise-Tolerant and from On-Line to Batch Learning Norbert Klasner, Hans Ulrich Simon

General Bounds on the Mutual Information Between a Parameter and N Conditionally Independent Observations David Haussler, Manfred Opper

Generalized Teaching Dimensions and the Query Complexity of Learning Tibor Hegedüs

How to Use Expert Advice in the Case When Actual Values of Estimated Events Remain Unknown Olga Mitina, Nikolai K. Vereshchagin

Inductive Inference of Functions on the Rationals Douglas A. Cenzer, William R. Moser

Language Learning from Texts: Mind Changes, Limited Memory and Monotonicity (Extended Abstract) Efim B. Kinber, Frank Stephan

Learning by a Population of Perceptrons Kukjin Kang, Jong-Hoon Oh

Learning DNF over the Uniform Distribution Using a Quantum Example Oracle Nader H. Bshouty, Jeffrey C. Jackson

Learning from a Mixture of Labeled and Unlabeled Examples with Parametric Side Information Joel Ratsaby, Santosh S. Venkatesh

Learning Internal Representations Jonathan Baxter

Learning to Model Sequences Generated by Switching Distributions Yoav Freund, Dana Ron

Learning to Reason with a Restricted View Roni Khardon, Dan Roth

Learning Using Group Representations (Extended Abstract) Dan Boneh

Learning via Queries and Oracles Frank Stephan

Learning with Unreliable Boundary Queries Avrim Blum, Prasad Chalasani, Sally A. Goldman, Donna K. Slonim

Markov Decision Processes in Large State Spaces Lawrence K. Saul, Satinder P. Singh

More or Less Efficient Agnostic Learning of Convex Polygons Paul Fischer

More Theorems About Scale-Sensitive Dimensions and Learning Peter L. Bartlett, Philip M. Long

Noise-Tolerant Parallel Learning of Geometric Concepts Nader H. Bshouty, Sally A. Goldman, H. David Mathias

On Efficient Agnostic Learning of Linear Combinations of Basis Functions Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

On Genetic Algorithms Eric B. Baum, Dan Boneh, Charles Garrett

On Learning Bounded-Width Branching Programs Funda Ergün, Ravi Kumar, Ronitt Rubinfeld

On Learning Decision Trees with Large Output Domains (Extended Abstract) Nader H. Bshouty, Christino Tamon, David K. Wilson

On Learning from Noisy and Incomplete Examples Scott E. Decatur, Rosario Gennaro

On Self-Directed Learning Shai Ben-David, Nadav Eiron, Eyal Kushilevitz

On the Inductive Inference of Real Valued Functions Kalvis Apsitis, Rusins Freivalds, Carl H. Smith

On the Learnability and Usage of Acyclic Probabilistic Finite Automata Dana Ron, Yoram Singer, Naftali Tishby

On the Learnability of Zn-DNF Formulas (Extended Abstract) Nader H. Bshouty, Zhixiang Chen, Scott E. Decatur, Steven Homer

On the Optimal Capacity of Binary Neural Networks: Rigorous Combinatorial Approaches Jeong Han Kim, James R. Roche

On-Line Learning of Binary and N-Ary Relations over Multi-Dimensional Clusters Atsuyoshi Nakamura, Naoki Abe

Online Learning via Congregational Gradient Descent Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares

Piecemeal Graph Exploration by a Mobile Robot (Extended Abstract) Baruch Awerbuch, Margrit Betke, Ronald L. Rivest, Mona Singh

Predicting Nearly as Well as the Best Pruning of a Decision Tree David P. Helmbold, Robert E. Schapire

Predictive Hebbian Learning Terrence J. Sejnowski, Peter Dayan, P. Read Montague

Proper Learning Algorithm for Functions of K Terms Under Smooth Distributions Yoshifumi Sakai, Eiji Takimoto, Akira Maruoka

Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination Kenji Yamanishi

Rationality Leslie G. Valiant

Reducing the Number of Queries in Self-Directed Learning Yiqun Lisa Yin

Reductions for Learning via Queries William I. Gasarch, Geoffrey R. Hird

Regression NSS: An Alternative to Cross Validation Michael P. Perrone, Brian S. Blais

Sample Sizes for Sigmoidal Neural Networks John Shawe-Taylor

Sequential PAC Learning Dale Schuurmans, Russell Greiner

Simple Learning Algorithms Using Divide and Conquer Nader H. Bshouty

Specification and Simulation of Statistical Query Algorithms for Efficiency and Noise Tolerance Javed A. Aslam, Scott E. Decatur

The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds When Few Input Variables Are Relevant Jyrki Kivinen, Manfred K. Warmuth