Verfügbarkeit: Learning theory

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

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Bibliographische Detailangaben
Format:	Tagungsbericht Buch
Sprache:	English
Veröffentlicht:	Berlin [u.a.] Springer 2007
Schriftenreihe:	Lecture Notes in Computer Science 4539 : Lecture notes in artificial intelligence
Schlagworte:	Algorithmische Lerntheorie Konferenzschrift > 2007 > San Diego Calif.
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XII, 634 S. graph. Darst.
ISBN:	9783540729259 3540729259

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MARC


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Datensatz im Suchindex

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adam_text	Table Invited Presentations Property Testing: A Learning Theory Perspective Dana Ron Spectral Algorithms for Learning and Clustering Santosh S. Vempala Unsupervised, Semisupervised and Active Learning I Minimax Bounds for Active Learning Rui Stability of fc-Means Clustering Shai Ben-David, Margin Based Active Learning Maria-Fiorina Unsupervised, Semisupervised and Active Learning II Learning Large-Alphabet and Analog Circuits with Value Injection Queries Dana Angluin, James Aspnes, Jiang Chen, and Lev Reyzin Teaching Dimension and the Complexity of Active Learning Steve Hanneke Multi- Sham M. Statistical Learning Theory Aggregation by Exponential Weighting and Sharp Oracle Inequalities Arnak S. Occam's Hammer Gilles Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector Sylvain Arlot, Gilles Blanchard, X Table of Contents Suboptimality of Penalized Empirical Risk Minimization in Classification Guillaume Lecué Transductive Ran El- Yaniv and Dmitry Inductive Inference U-Shaped, Iterative, and Iterative-with-Counter Learning John Case and Samuel E. Moelius III Mind Change Optimal Learning of Oliver Learning Correction Grammars Lorenzo Carlucci, John Case, and Sanjay Jain Mitotic Classes Sanjay Jain and Frank Online and Reinforcement Learning I Regret to the Best vs. Regret to the Average Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman Strategies for Prediction Under Imperfect Monitoring Gábor Lugosi, Bounded Parameter Markov Decision Processes with Average Reward Criterion Ambuj Tewari and Peter L. Bartlett Online and Reinforcement Learning II On-Line Estimation with the Multivariate Gaussian Distribution Sanjoy Dasgupta and Daniel Hsu Generalised Entropy and Asymptotic Complexities of Languages Yuri Kalnishkan, Vladimir Vovk, and Michael V. Vyugin Q- Learning with Linear Function Approximation Francisco S. Regularized Learning, Kernel Methods, SVM How Good Is a Kernel When Used as a Similarity Measure? Nathan Table Gaps in Support Vector Optimization Nikolas List, Don Hush, Clint Scovel, and Learning Languages with Rational Kernels Corinna Generalized SMO-Style Decomposition Algorithms Nikolas List Learning Algorithms and Limitations on Learning Learning Nested Adam Tauman Kalai An Efficient Re-scaled Perception Algorithm for Conic Systems Alexandre A Lower Bound for Agnostically Learning Disjunctions Adam R. Klivans and Alexander A. Sherstov Sketching Information Divergences Sudipto Guha, Online and Reinforcement Learning III Competing with Stationary Prediction Strategies Vladimir Vovk Improved Rates for the Stochastic Continuum-Armed Bandit Problem Peter Auer, Ronald Ortner, and Learning Permutations with Exponential Weights David P. Online and Reinforcement Learning IV Multitask Learning with Expert Advice Jacob Abernethy, Peter Bartlett, and Alexander Rakhlin Online Learning with Prior Knowledge Elad Hazán Dimensionality Reduction Nonlinear Estimators and Tail Bounds for Dimension Reduction in l\ Using Cauchy Random Projections Ping Li, Trevor J. XII Table of Sparse Florentina Bunea, Alexandre l\ Regularization in Infinite Dimensional Feature Spaces Saharon Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking Sivan Other Approaches Observational Learning in Random Networks Julian The Loss Rank Principle for Model Selection Marcus Hutter Robust Reductions from Ranking to Classification Maria-Fiorina Don Coppersmith, John Open Problems Rademacher Margin Complexity Liwei Wang and Jufu Feng Open Problems in Efficient Semi-supervised Avrim Blum and Maria-Fiorina Resource-Bounded Information Gathering for Correlation Clustering Pallika Kanani and Andrew McCallum Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? Nathan When Is There a Free Matrix Lunch? Manfred K. Warmuth Author Index
adam_txt	Table Invited Presentations Property Testing: A Learning Theory Perspective Dana Ron Spectral Algorithms for Learning and Clustering Santosh S. Vempala Unsupervised, Semisupervised and Active Learning I Minimax Bounds for Active Learning Rui Stability of fc-Means Clustering Shai Ben-David, Margin Based Active Learning Maria-Fiorina Unsupervised, Semisupervised and Active Learning II Learning Large-Alphabet and Analog Circuits with Value Injection Queries Dana Angluin, James Aspnes, Jiang Chen, and Lev Reyzin Teaching Dimension and the Complexity of Active Learning Steve Hanneke Multi- Sham M. Statistical Learning Theory Aggregation by Exponential Weighting and Sharp Oracle Inequalities Arnak S. Occam's Hammer Gilles Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector Sylvain Arlot, Gilles Blanchard, X Table of Contents Suboptimality of Penalized Empirical Risk Minimization in Classification Guillaume Lecué Transductive Ran El- Yaniv and Dmitry Inductive Inference U-Shaped, Iterative, and Iterative-with-Counter Learning John Case and Samuel E. Moelius III Mind Change Optimal Learning of Oliver Learning Correction Grammars Lorenzo Carlucci, John Case, and Sanjay Jain Mitotic Classes Sanjay Jain and Frank Online and Reinforcement Learning I Regret to the Best vs. Regret to the Average Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman Strategies for Prediction Under Imperfect Monitoring Gábor Lugosi, Bounded Parameter Markov Decision Processes with Average Reward Criterion Ambuj Tewari and Peter L. Bartlett Online and Reinforcement Learning II On-Line Estimation with the Multivariate Gaussian Distribution Sanjoy Dasgupta and Daniel Hsu Generalised Entropy and Asymptotic Complexities of Languages Yuri Kalnishkan, Vladimir Vovk, and Michael V. Vyugin Q- Learning with Linear Function Approximation Francisco S. Regularized Learning, Kernel Methods, SVM How Good Is a Kernel When Used as a Similarity Measure? Nathan Table Gaps in Support Vector Optimization Nikolas List, Don Hush, Clint Scovel, and Learning Languages with Rational Kernels Corinna Generalized SMO-Style Decomposition Algorithms Nikolas List Learning Algorithms and Limitations on Learning Learning Nested Adam Tauman Kalai An Efficient Re-scaled Perception Algorithm for Conic Systems Alexandre A Lower Bound for Agnostically Learning Disjunctions Adam R. Klivans and Alexander A. Sherstov Sketching Information Divergences Sudipto Guha, Online and Reinforcement Learning III Competing with Stationary Prediction Strategies Vladimir Vovk Improved Rates for the Stochastic Continuum-Armed Bandit Problem Peter Auer, Ronald Ortner, and Learning Permutations with Exponential Weights David P. Online and Reinforcement Learning IV Multitask Learning with Expert Advice Jacob Abernethy, Peter Bartlett, and Alexander Rakhlin Online Learning with Prior Knowledge Elad Hazán Dimensionality Reduction Nonlinear Estimators and Tail Bounds for Dimension Reduction in l\ Using Cauchy Random Projections Ping Li, Trevor J. XII Table of Sparse Florentina Bunea, Alexandre l\ Regularization in Infinite Dimensional Feature Spaces Saharon Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking Sivan Other Approaches Observational Learning in Random Networks Julian The Loss Rank Principle for Model Selection Marcus Hutter Robust Reductions from Ranking to Classification Maria-Fiorina Don Coppersmith, John Open Problems Rademacher Margin Complexity Liwei Wang and Jufu Feng Open Problems in Efficient Semi-supervised Avrim Blum and Maria-Fiorina Resource-Bounded Information Gathering for Correlation Clustering Pallika Kanani and Andrew McCallum Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? Nathan When Is There a Free Matrix Lunch? Manfred K. Warmuth Author Index
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spelling	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings Nader H. Bshouty ... (eds.) Berlin [u.a.] Springer 2007 XII, 634 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Lecture Notes in Computer Science 4539 : Lecture notes in artificial intelligence Algorithmische Lerntheorie (DE-588)4701014-9 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2007 San Diego Calif. gnd-content Algorithmische Lerntheorie (DE-588)4701014-9 s DE-604 Bshouty, Nader H. Sonstige oth Conference on Learning Theory 20 2007 San Diego, Calif. Sonstige (DE-588)6516166-X oth Lecture Notes in Computer Science 4539 : Lecture notes in artificial intelligence (DE-604)BV000000607 4539 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015692433&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings Lecture Notes in Computer Science Algorithmische Lerntheorie (DE-588)4701014-9 gnd
subject_GND	(DE-588)4701014-9 (DE-588)1071861417
title	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings
title_auth	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings
title_exact_search	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings
title_exact_search_txtP	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings
title_full	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings Nader H. Bshouty ... (eds.)
title_fullStr	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings Nader H. Bshouty ... (eds.)
title_full_unstemmed	Learning theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings Nader H. Bshouty ... (eds.)
title_short	Learning theory
title_sort	learning theory 20th annual conference on learning theory colt 2007 san diego ca usa june 13 15 2007 proceedings
title_sub	20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 ; proceedings
topic	Algorithmische Lerntheorie (DE-588)4701014-9 gnd
topic_facet	Algorithmische Lerntheorie Konferenzschrift 2007 San Diego Calif.
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015692433&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV000000607
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