Learning Theory

19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings

By: Hans Ulrich Simon (Editor), GÃ¡bor Lugosi (Editor)

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Paperback | 12 June 2006

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676 Pages

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This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006.

The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more.

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Random multivariate search Trees	p. 1
On learning and logic	p. 2
Predictions as statements and decisions	p. 4
A sober look at clustering stability	p. 5
PAC learning axis-aligned mixtures of Gaussians with no separation assumption	p. 20
Stable transductive learning	p. 35
Uniform convergence of adaptive graph-based regularization	p. 50
The rademacher complexity of linear transformation classes	p. 65
Function classes that approximate the bayes risk	p. 79
Functional classification with margin conditions	p. 94
Significance and recovery of block structures in binary matrices with noise	p. 109
Maximum entropy distribution estimation with generalized regularization	p. 123
Unifying divergence minimization and statistical inference via convex duality	p. 139
Mercer's theorem, feature maps, and smoothing	p. 154
Learning bounds for support vector machines with learned kernels	p. 169
On optimal learning algorithms for multiplicity automata	p. 184
Exact learning composed classes with a small number of mistakes	p. 199
DNF are teachable in the average case	p. 214
Teaching randomized learners	p. 229
Memory-limited U-shaped learning	p. 244
On learning languages from positive data and a limited number of short counterexamples	p. 259
Learning rational stochastic languages	p. 274
Parent assignment is hard for the MDL, AIC, and NML costs	p. 289
Uniform-distribution learnability of noisy linear threshold functions with restricted focus of attention	p. 304
Discriminative learning can succeed where generative learning fails	p. 319
Improved lower bounds for learning intersections of halfspaces	p. 335
Efficient learning algorithms yield circuit lower bounds	p. 350
Optimal oracle inequality for aggregation of classifiers under low noise condition	p. 364
Aggregation and sparsity via l[subscript 1] penalized least squares	p. 379
A randomized online learning algorithm for better variance control	p. 392
Online learning with variable stage duration	p. 408
Online learning meets optimization in the dual	p. 423
Online tracking of linear subspaces	p. 438
Online multitask learning	p. 453
The shortest path problem under partial monitoring	p. 468
Tracking the best hyperplane with a simple budget perceptron	p. 483
Logarithmic regret algorithms for online convex optimization	p. 499
Online variance minimization	p. 514
Online learning with constraints	p. 529
Continuous experts and the binning algorithm	p. 544
Competing with wild prediction rules	p. 559
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path	p. 574
Ranking with a P-norm push	p. 589
Subset ranking using regression	p. 605
Active sampling for multiple output identification	p. 620
Improving random projections using marginal information	p. 635
Efficient algorithms for general active learning	p. 650
Can entropic regularization be replaced by squared Euclidean distance plus additional linear constraints	p. 653
Table of Contents provided by Blackwell. All Rights Reserved.

Learning Theory

19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings

At a Glance

Paperback

Shipping

How to return your order

Defective items

You Can Find This Book In

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