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Learning theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 : proceedings

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Cover of 'Learning theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 : proceedings'

Table of Contents

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    Book Overview
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    Chapter 1 Property Testing: A Learning Theory Perspective
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    Chapter 2 Spectral Algorithms for Learning and Clustering
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    Chapter 3 Minimax Bounds for Active Learning
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    Chapter 4 Stability of k-Means Clustering
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    Chapter 5 Margin Based Active Learning
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    Chapter 6 Learning Large-Alphabet and Analog Circuits with Value Injection Queries
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    Chapter 7 Teaching Dimension and the Complexity of Active Learning
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    Chapter 8 Multi-view Regression Via Canonical Correlation Analysis
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    Chapter 9 Aggregation by Exponential Weighting and Sharp Oracle Inequalities
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    Chapter 10 Occam’s Hammer
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    Chapter 11 Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector
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    Chapter 12 Suboptimality of Penalized Empirical Risk Minimization in Classification
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    Chapter 13 Transductive Rademacher Complexity and Its Applications
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    Chapter 14 U-Shaped, Iterative, and Iterative-with-Counter Learning
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    Chapter 15 Mind Change Optimal Learning of Bayes Net Structure
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    Chapter 16 Learning Correction Grammars
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    Chapter 17 Mitotic Classes
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    Chapter 18 Regret to the Best vs. Regret to the Average
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    Chapter 19 Strategies for Prediction Under Imperfect Monitoring
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    Chapter 20 Bounded Parameter Markov Decision Processes with Average Reward Criterion
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    Chapter 21 On-Line Estimation with the Multivariate Gaussian Distribution
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    Chapter 22 Generalised Entropy and Asymptotic Complexities of Languages
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    Chapter 23 Q-Learning with Linear Function Approximation
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    Chapter 24 How Good Is a Kernel When Used as a Similarity Measure?
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    Chapter 25 Gaps in Support Vector Optimization
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    Chapter 26 Learning Languages with Rational Kernels
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    Chapter 27 Generalized SMO-Style Decomposition Algorithms
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    Chapter 28 Learning Nested Halfspaces and Uphill Decision Trees
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    Chapter 29 Learning Theory
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    Chapter 30 A Lower Bound for Agnostically Learning Disjunctions
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    Chapter 31 Sketching Information Divergences
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    Chapter 32 Competing with Stationary Prediction Strategies
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    Chapter 33 Improved Rates for the Stochastic Continuum-Armed Bandit Problem
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    Chapter 34 Learning Permutations with Exponential Weights
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    Chapter 35 Multitask Learning with Expert Advice
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    Chapter 36 Online Learning with Prior Knowledge
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    Chapter 37 Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections
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    Chapter 38 Sparse Density Estimation with ℓ1 Penalties
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    Chapter 39 ℓ1 Regularization in Infinite Dimensional Feature Spaces
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    Chapter 40 Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking
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    Chapter 41 Observational Learning in Random Networks
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    Chapter 42 The Loss Rank Principle for Model Selection
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    Chapter 43 Robust Reductions from Ranking to Classification
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    Chapter 44 Rademacher Margin Complexity
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    Chapter 45 Open Problems in Efficient Semi-supervised PAC Learning
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    Chapter 46 Resource-Bounded Information Gathering for Correlation Clustering
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    Chapter 47 Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?
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    Chapter 48 When Is There a Free Matrix Lunch?
Overall attention for this book and its chapters
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Citations

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Title
Learning theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 : proceedings
Published by
Springer, January 2007
DOI 10.1007/978-3-540-72927-3
ISBNs
978-3-54-072925-9, 978-3-54-072927-3
Authors

Bshouty, Nader H, Gentile, Claudio

Editors

Nader H. Bshouty, Claudio Gentile

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 4%
Student > Bachelor 1 2%
Student > Doctoral Student 1 2%
Student > Master 1 2%
Unknown 48 91%
Readers by discipline Count As %
Computer Science 4 8%
Business, Management and Accounting 1 2%
Unknown 48 91%