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Learning Theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA; June 13-15, 2007. Proceedings / edited by Nader H. Bshouty, Claudio Gentile
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Lecture notes in computer science ; 4539Publisher: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2007Description: Online-Ressource (XI, 634 p. Also available online, digital)ISBN:- 9783540729273
- 004.0151
- 006.3 23
- 004
- QA75.5-76.95
- Q325.5
Contents:
Summary: Invited Presentations -- Property Testing: A Learning Theory Perspective -- Spectral Algorithms for Learning and Clustering -- Unsupervised, Semisupervised and Active Learning I -- Minimax Bounds for Active Learning -- Stability of k-Means Clustering -- Margin Based Active Learning -- Unsupervised, Semisupervised and Active Learning II -- Learning Large-Alphabet and Analog Circuits with Value Injection Queries -- Teaching Dimension and the Complexity of Active Learning -- Multi-view Regression Via Canonical Correlation Analysis -- Statistical Learning Theory -- Aggregation by Exponential Weighting and Sharp Oracle Inequalities -- Occam’s Hammer -- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector -- Suboptimality of Penalized Empirical Risk Minimization in Classification -- Transductive Rademacher Complexity and Its Applications -- Inductive Inference -- U-Shaped, Iterative, and Iterative-with-Counter Learning -- Mind Change Optimal Learning of Bayes Net Structure -- Learning Correction Grammars -- Mitotic Classes -- Online and Reinforcement Learning I -- Regret to the Best vs. Regret to the Average -- Strategies for Prediction Under Imperfect Monitoring -- Bounded Parameter Markov Decision Processes with Average Reward Criterion -- Online and Reinforcement Learning II -- On-Line Estimation with the Multivariate Gaussian Distribution -- Generalised Entropy and Asymptotic Complexities of Languages -- Q-Learning with Linear Function Approximation -- Regularized Learning, Kernel Methods, SVM -- How Good Is a Kernel When Used as a Similarity Measure? -- Gaps in Support Vector Optimization -- Learning Languages with Rational Kernels -- Generalized SMO-Style Decomposition Algorithms -- Learning Algorithms and Limitations on Learning -- Learning Nested Halfspaces and Uphill Decision Trees -- An Efficient Re-scaled Perceptron Algorithm for Conic Systems -- A Lower Bound for Agnostically Learning Disjunctions -- Sketching Information Divergences -- Competing with Stationary Prediction Strategies -- Online and Reinforcement Learning III -- Improved Rates for the Stochastic Continuum-Armed Bandit Problem -- Learning Permutations with Exponential Weights -- Online and Reinforcement Learning IV -- Multitask Learning with Expert Advice -- Online Learning with Prior Knowledge -- Dimensionality Reduction -- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections -- Sparse Density Estimation with ?1 Penalties -- ?1 Regularization in Infinite Dimensional Feature Spaces -- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking -- Other Approaches -- Observational Learning in Random Networks -- The Loss Rank Principle for Model Selection -- Robust Reductions from Ranking to Classification -- Open Problems -- Rademacher Margin Complexity -- Open Problems in Efficient Semi-supervised PAC Learning -- Resource-Bounded Information Gathering for Correlation Clustering -- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? -- When Is There a Free Matrix Lunch?.PPN: PPN: 1646881214Package identifier: Produktsigel: ZDB-2-LNC | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-SEB
""Title page""; ""Preface""; ""Organization""; ""Table of Contents""; ""Property Testing:A Learning Theory Perspective""; ""Spectral Algorithms for Learning and Clustering""; ""Summary""; ""Mixture Models""; ""Clustering from Similarities""; ""Minimax Bounds for Active Learning""; ""Introduction""; ""Problem Formulation""; ""Lower Bounds""; ""Upper Bounds""; ""Final Remarks""; ""Proof of Theorem 1""; ""Proof of Theorem 2""; ""Stability of k-Means Clustering""; ""Introduction""; ""Definitions""; ""Stability of Risk Optimizing Clustering Algorithms""; ""Proof Outline""; ""The Technical Lemmas""
""Conclusions and Discussion""""Margin Based Active Learning""; ""Introduction""; ""Definitions and Notation""; ""The Realizable Case Under the Uniform Distribution""; ""The Non-realizable Case Under the Uniform Distribution""; ""Dimension Independent Bounds""; ""A General Analysis for Margin Based Active Learning""; ""Discussion and Open Problems""; "" Useful Facts""; ""Probability Estimation in High Dimensional Ball""; ""Learning Large-Alphabet and Analog Circuits with Value Injection Queries""; ""Introduction""; ""Preliminaries""; ""Circuits""; ""Experiments on Circuits""
""The Learning Problems""""Learning Large-Alphabet Circuits""; ""Hardness for Large Alphabets with Unrestricted Topology""; ""Distinguishing Paths""; ""The Distinguishing Paths Algorithm""; ""The Shortcuts Algorithm""; ""Learning Analog Circuits Via Discretization""; ""A Lipschitz Condition""; ""Discretizing Analog Circuits""; ""Learning with Experiments and Counterexamples""; ""The Learning Algorithm""; ""A New Diagnosis Algorithm""; ""Teaching Dimension and the Complexity of Active Learning""; ""Overview of Main Results""; ""Context and Related Work""; ""Notation""
""Extended Teaching Dimension""""The Complexity of Realizable Active Learning""; ""The Complexity of Active Learning with Noise""; ""Lower Bounds""; ""Example: Axis-Aligned Rectangles""; ""Open Problems""; ""Multi-view Regression Via Canonical Correlation Analysis""; ""Introduction""; ""Preliminaries""; ""Regression with Multiple Views""; ""CCA and the Canonical Basis""; ""Learning""; ""A Shrinkage Estimator (Via Ridge Regression)""; ""A (Possibly) Lower Dimensional Estimator""; ""The Bias-Variance Tradeoff""; ""Discussion""
""Aggregation by Exponential Weighting and Sharp Oracle Inequalities""""Introduction""; ""Risk Bounds for n-Divisible Distributions of Errors""; ""Model Selection with Finite or CountableLAMBDA ""; ""Checking Assumptions (A) and (B)""; ""Risk Bounds for General Distributions of Errors""; ""Sparsity Oracle Inequality""; ""Appendix""; ""Occam�s Hammer""; ""Introduction""; ""Main Result""; ""Setting""; ""False Prediction Rate""; ""Warming Up: Algorithm with Constant Volume Output""; ""General Case""; ""Applications""; ""Randomized Classifiers: An Alternate Look at PAC-Bayes Bounds""
""Multiple Testing: A Family of ``Step-Up'' Algorithms with Distribution-Free FDR Control""
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