COLT
Klappentext
COLT
Inhalt
Foreword
Invited Talks
Learning and Generalization
The Role of Learning in Autonomous Robots
Session 1: Morning, Aug 5
Tracking Drifting Concepts Using Random Examples
Investigating the Distribution Assumptions in the Pac Learning Model
Simultaneous Learning of Concepts and Simultaneous Estimation of Probabilities
Learning by Smoothing: A Morphological Approach
Session 2:
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
Calculation of the Learning Curve of Bayes Optimal Classification Algorithm for Learning a Perceptron with Noise
Probably Almost Bayes Decisions
Session 3: Afternoon, Aug 5
Invited Talk
Learning and Generalization
Session 4:
A Geometric Approach to Threshold Circuit Complexity
Learning Curves in Large Neural Networks
On the Learnability of Infinitary Regular Sets
Session 5: Morning, Aug 6
Learning Monotone DNF with an Incomplete Membership Oracle
Redundant Noisy Attributes, Attribute Errors, and Linear-Threshold Learning Using Winnow
Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes
On-Line Learning with an Oblivious Environment and the Power of Randomization
Session 6:
Learning Monotone kµ-DNF Formulas on Product Distributions
Learning Probabilistic Read-Once Formulas on Product Distributions
Learning 2µ-DNF Formulas and kµ Decision Trees
Session 7: Afternoon, Aug 6
Invited Talk
The Role of Learning in Autonomous Robots
Session 8:
Polynomial-Time Learning of Very Simple Grammars from Positive Data
Relations Between Probabilistic and Team One-Shot Learners
When Oracles Do Not Help
Session 9: Morning, Aug 7
Approximation and Estimation Bounds for Artificial Neural Networks
The VC-Dimension Vs. the Statistical Capacity for Two Layer Networks with Binary Weights
On Learning Binary Weights for Majority Functions
Evaluating the Performance of a Simple Inductive Procedure in the Presence of Overfitting Error
Session 10:
Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence
A Loss Bound Model for On-Line Stochastic Prediction Strategies
On the Complexity of Teaching
Session 11: Afternoon, Aug 7
Improved Learning of AC0 Functions
Learning Read-Once Formulas Over Fields and Extended Bases
Fast Identification of Geometric Objects with Membership Queries
Bounded Degree Graph Inference from Walks
On the Complexity of Learning Strings and Sequences
The Correct Definition of Finite Elasticity: Corrigendum to Identification of Unions
Author Index