LEADER 04970oam 2200589 450 001 9910767552603321 005 20210520213533.0 010 $a1-280-94078-6 010 $a9786610940783 010 $a3-540-72927-5 024 7 $a10.1007/978-3-540-72927-3 035 $a(CKB)1000000000478525 035 $a(EBL)3061502 035 $a(SSID)ssj0000190640 035 $a(PQKBManifestationID)11199414 035 $a(PQKBTitleCode)TC0000190640 035 $a(PQKBWorkID)10180718 035 $a(PQKB)10646709 035 $a(DE-He213)978-3-540-72927-3 035 $a(MiAaPQ)EBC3061502 035 $a(MiAaPQ)EBC6413326 035 $a(PPN)12316284X 035 $a(EXLCZ)991000000000478525 100 $a20210520d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aLearning theory $e20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007 : proceedings /$fNader H. Bshouty, Claudio Gentile (editors) 205 $a1st ed. 2007. 210 1$aBerlin, Germany :$cSpringer,$d[2007] 210 4$d©2007 215 $a1 online resource (644 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v4539 300 $aDescription based upon print version of record. 311 $a3-540-72925-9 320 $aIncludes bibliographical references and index. 327 $aInvited 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?. 410 0$aLecture Notes in Artificial Intelligence ;$v4539 606 $aMachine learning$vCongresses 615 0$aMachine learning 676 $a006.31 702 $aBshouty$b Nader H. 702 $aGentile$b Claudio 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910767552603321 996 $aLearning Theory$9772233 997 $aUNINA