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Home > Proceedings of the Tenth Annual Conference on Computational Learning Theory: July 6th-9th, 1997, Nashville, Tennessee / > Learning Distributions from Random Walks
Proceedings of the Tenth Annual Conference on Computational Learning Theory: July 6th-9th, 1997, Nashville, Tennessee / |
Acm Special Interest Group for Automata
Association for Computer Machinery,
Proceedings of the Tenth Annual Conference on Computational Learning Theory: July 6th-9th, 1997, Nashville, Tennessee /
Learning Distributions from Random Walks
Authors

Funda Ergun, S. Ravi Kumar and Ronitt Rubinfeld

ARI Id

1675198588197_52008900

Access

Open/Free Access

Table of Contents of Book
Showing 1 to 20 of 35 entries
Chapters/HeadingsAuthor(s)PagesInfo
Information Theory in Probability, Statistics, Learning, and Neural Nets
Andrew Barron
A PAC Analysis of a Bayesian Estimator
John Shawe-Taylor and Robert C. Williamson
Learning Logic Programs by Using the Product Homomorphism Method
Tamas Horvdth, Robert H. Sloan and Gyorgy Turan
On-line Evaluation and Prediction Using Linear Functions
Philip M. Long
Derandomizing Stochastic Prediction Strategies
V. Vovk
On-line Learning and the Metrical Task System Problem
Avrim Blum and Carl Burch
On the Complexity of Learning for a Spiking Neuron
Wolfgang Maass and Michael Schmitt
Learning Probabilistically Consistent Linear Threshold Functions
Tom Bylander
PAC Adaptive Control of Linear Systems
Claude-Nicolas Fiechter
FINite Learning Capabilities and Their Limits
Robert Daley and Bala Kalyanasundaram
Asymmetric Team Learning
Kalvis Apsitis, Rusins Freivalds and Carl H. Smith
Generalized Notions of Mind Change Complexity
Arun Sharma, Frank Stephan and Yuri Ventsov
A Brief Look at Some Machine Learning Problems in Genomics
David Haussler
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees
Fernando Pereira and Yoram Singer
Performance Bounds for Nonlinear Time Series Prediction
Ron Meir
Computational Sample Complexity
Scott Decatur, Oded Goldreich and Dana Ron
Dense Shattering and Teaching Dimensions for Differentiable Families
A. Kowalczyk
Algorithmic Stability and Sanity-check Bounds for Leave-one-out Cross-validation
Michael Kearns and Dana Ron
Analysis of Two Gradient-based Algorithms for On-line Regression
Nicolo Cesa-Bianchi
General Convergence Results for Linear Discriminant Updates
Adam J. Grove, Nick Littlestone and Dale Schuurmans
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 35 entries
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