COLT 1992

48 papers

A Computational Model of Teaching Jeffrey C. Jackson, Andrew Tomkins

A Noise Model on Learning Sets of Strings Yasubumi Sakakibara, Rani Siromoney

A Technique for Upper Bounding the Spectral Norm with Applications to Learning Mihir Bellare

A Theory for Memory-Based Learning Jyh-Han Lin, Jeffrey Scott Vitter

A Training Algorithm for Optimal Margin Classifiers Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik

Absolute Error Bounds for Learning Linear Functions Online Ethan Bernstein

An Improved Boosting Algorithm and Its Implications on Learning Complexity Yoav Freund

An O(nlog Log N) Learning Algorithm for DNF Under the Uniform Distribution Yishay Mansour

Approximate Testing and Learnability Kathleen Romanik

Bellman Strikes Again! the Growth Rate of Sample Complexity with Dimension for the Nearest Neighbor Classifier Santosh S. Venkatesh, Robert R. Snapp, Demetri Psaltis

Breaking the Probability 1/2 Barrier in FIN-Type Learning Robert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai

Case-Based Learning in Inductive Inference Klaus P. Jantke

Characterizations of Learnability for Classes of O, ..., N-Valued Functions Shai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long

Corrigendum to Types of Noise in Data for Concept Learning Robert H. Sloan

Cryptographic Lower Bounds for Learnability of Boolean Functions on the Uniform Distribution Michael Kharitonov

Degrees of Inferability Peter Cholak, Efim B. Kinber, Rodney G. Downey, Martin Kummer, Lance Fortnow, Stuart A. Kurtz, William I. Gasarch, Theodore A. Slaman

Dominating Distributions and Learnability Gyora M. Benedek, Alon Itai

Exact Learning of Read-K Disjoint DNF and Not-so-Disjoint DNF Howard Aizenstein, Leonard Pitt

Generalization Versus Classification Rolf Wiehagen, Carl H. Smith

Language Learning from Stochastic Input Shyam Kapur, Gianfranco Bilardi

Learnability of Description Logics William W. Cohen, Haym Hirsh

Learning Boolean Read-Once Formulas with Arbitrary Symmetric and Constant Fan-in Gates Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein

Learning DNF Formulae Under Classes of Probability Distributions Michele Flammini, Alberto Marchetti-Spaccamela, Ludek Kucera

Learning Hierarchical Rule Sets Jyrki Kivinen, Heikki Mannila, Esko Ukkonen

Learning K-Term DNF Formulas with an Incomplete Membership Oracle Sally A. Goldman, H. David Mathias

Learning Stochastic Functions by Smooth Simultaneous Estimation Kevin Buescher, P. R. Kumar

Learning Switching Concepts Avrim Blum, Prasad Chalasani

Learning with a Slowly Changing Distribution Peter L. Bartlett

On Exact Specification by Examples Martin Anthony, Graham R. Brightwell, David A. Cohen, John Shawe-Taylor

On Learning Limiting Programs John Case, Sanjay Jain, Arun Sharma

On Learning Noisy Threshold Functions with Finite Precision Weights Ronny Meir, José F. Fontanari

On the Computational Power of Neural Nets Hava T. Siegelmann, Eduardo D. Sontag

On the Role of Procrastination for Machine Learning Rusins Freivalds, Carl H. Smith

On-Line Learning of Rectangles Zhixiang Chen, Wolfgang Maass

PAB-Decisions for Boolean and Real-Valued Features Svetlana Anoulova, Paul Fischer, Stefan Pölt, Hans Ulrich Simon

PAC Learning with Generalized Samples and an Application to Stochastic Geometry Sanjeev R. Kulkarni, John N. Tsitsiklis, Sanjoy K. Mitter, Ofer Zeitouni

PAC-Learnability of Determinate Logic Programs Saso Dzeroski, Stephen H. Muggleton, Stuart Russell

Polynomial Iniform Convergence and Polynomial-Sample Learnability Alberto Bertoni, Paola Campadelli, Anna Morpurgo, Sandra Panizza

Polynomial Time Inference of a Subclass of Context-Free Transformations Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara

Probably Almost Discriminative Learning Kenji Yamanishi

Query by Committee H. Sebastian Seung, Manfred Opper, Haim Sompolinsky

Random DFA's Can Be Approximately Learned from Sparse Uniform Examples Kevin J. Lang

Robust Trainability of Single Neurons Klaus-Uwe Höffgen, Hans Ulrich Simon

Some Weak Learning Results David P. Helmbold, Manfred K. Warmuth

The Learning Complexity of Smooth Functions of a Single Variable Don Kimber, Philip M. Long

Toward Efficient Agnostic Learning Michael J. Kearns, Robert E. Schapire, Linda Sellie

Types of Monotonic Language Learning and Their Characterization Steffen Lange, Thomas Zeugmann

Universal Sequential Learning and Decision from Individual Data Sequences Neri Merhav, Meir Feder