Learning Theory : 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings / edited by Peter Auer, Ron Meir.

Format:

Book

Contributor:

Auer, Peter, editor.

Meir, Ron, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 3559.

Lecture Notes in Artificial Intelligence ; 3559

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Algorithms.

Logic, Symbolic and mathematical.

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Local Subjects:

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Physical Description:

1 online resource (XII, 692 pages).

Edition:

First edition 2005.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.

System Details:

text file PDF

Summary:

This volume contains papers presented at the Eighteenth Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in Bertinoro, Italy from June 27 to 30, 2005. The technical program contained 45 papers selected from 120 submissions, 3 open problems selected from among 5 contributed, and 2 invited lectures. The invited lectures were given by Sergiu Hart on "Uncoupled Dynamics and Nash Equilibrium", and by Satinder Singh on "Rethinking State, Action, and Reward in Reinforcement Learning". These papers were not included in this volume. The Mark Fulk Award is presented annually for the best paper co-authored by a student. The student selected this year was Hadi Salmasian for the paper titled "The Spectral Method for General Mixture Models" co-authored with Ravindran Kannan and Santosh Vempala. The number of papers submitted to COLT this year was exceptionally high. In addition to the classical COLT topics, we found an increase in the number of submissions related to novel classi?cation scenarios such as ranking. This - crease re?ects a healthy shift towards more structured classi?cation problems, which are becoming increasingly relevant to practitioners.

Contents:

Learning to Rank

Ranking and Scoring Using Empirical Risk Minimization

Learnability of Bipartite Ranking Functions

Stability and Generalization of Bipartite Ranking Algorithms

Loss Bounds for Online Category Ranking

Boosting

Margin-Based Ranking Meets Boosting in the Middle

Martingale Boosting

The Value of Agreement, a New Boosting Algorithm

Unlabeled Data, Multiclass Classification

A PAC-Style Model for Learning from Labeled and Unlabeled Data

Generalization Error Bounds Using Unlabeled Data

On the Consistency of Multiclass Classification Methods

Sensitive Error Correcting Output Codes

Online Learning I

Data Dependent Concentration Bounds for Sequential Prediction Algorithms

The Weak Aggregating Algorithm and Weak Mixability

Tracking the Best of Many Experts

Improved Second-Order Bounds for Prediction with Expert Advice

Online Learning II

Competitive Collaborative Learning

Analysis of Perceptron-Based Active Learning

A New Perspective on an Old Perceptron Algorithm

Support Vector Machines

Fast Rates for Support Vector Machines

Exponential Convergence Rates in Classification

General Polynomial Time Decomposition Algorithms

Kernels and Embeddings

Approximating a Gram Matrix for Improved Kernel-Based Learning

Learning Convex Combinations of Continuously Parameterized Basic Kernels

On the Limitations of Embedding Methods

Leaving the Span

Inductive Inference

Variations on U-Shaped Learning

Mind Change Efficient Learning

On a Syntactic Characterization of Classification with a Mind Change Bound

Unsupervised Learning

Ellipsoid Approximation Using Random Vectors

The Spectral Method for General Mixture Models

On Spectral Learning of Mixtures of Distributions

From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians

Towards a Theoretical Foundation for Laplacian-Based Manifold Methods

Generalization Bounds

Permutation Tests for Classification

Localized Upper and Lower Bounds for Some Estimation Problems

Improved Minimax Bounds on the Test and Training Distortion of Empirically Designed Vector Quantizers

Rank, Trace-Norm and Max-Norm

Query Learning, Attribute Efficiency, Compression Schemes

Learning a Hidden Hypergraph

On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions

Unlabeled Compression Schemes for Maximum Classes

Economics and Game Theory

Trading in Markovian Price Models

From External to Internal Regret

Separation Results for Learning Models

Separating Models of Learning from Correlated and Uncorrelated Data

Asymptotic Log-Loss of Prequential Maximum Likelihood Codes

Teaching Classes with High Teaching Dimension Using Few Examples

Open Problems

Optimum Follow the Leader Algorithm

The Cross Validation Problem

Compute Inclusion Depth of a Pattern.

Other Format:

Printed edition:

ISBN:

978-3-540-31892-7

9783540318927

Access Restriction:

Restricted for use by site license.