LEADER 05751nam 22006855 450 001 9910767544303321 005 20200629201012.0 010 $a3-540-27819-2 024 7 $a10.1007/b98522 035 $a(CKB)1000000000210170 035 $a(SSID)ssj0000190641 035 $a(PQKBManifestationID)11215644 035 $a(PQKBTitleCode)TC0000190641 035 $a(PQKBWorkID)10180719 035 $a(PQKB)11723536 035 $a(DE-He213)978-3-540-27819-1 035 $a(MiAaPQ)EBC3088488 035 $a(PPN)155179225 035 $a(EXLCZ)991000000000210170 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aLearning Theory $e17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings /$fedited by John Shawe-Taylor, Yoram Singer 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (X, 654 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3120 300 $aIncludes index. 311 $a3-540-22282-0 327 $aEconomics and Game Theory -- Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions -- Graphical Economics -- Deterministic Calibration and Nash Equilibrium -- Reinforcement Learning for Average Reward Zero-Sum Games -- OnLine Learning -- Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability -- Minimizing Regret with Label Efficient Prediction -- Regret Bounds for Hierarchical Classification with Linear-Threshold Functions -- Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary -- Inductive Inference -- Learning Classes of Probabilistic Automata -- On the Learnability of E-pattern Languages over Small Alphabets -- Replacing Limit Learners with Equally Powerful One-Shot Query Learners -- Probabilistic Models -- Concentration Bounds for Unigrams Language Model -- Inferring Mixtures of Markov Chains -- Boolean Function Learning -- PExact = Exact Learning -- Learning a Hidden Graph Using O(log n) Queries Per Edge -- Toward Attribute Efficient Learning of Decision Lists and Parities -- Empirical Processes -- Learning Over Compact Metric Spaces -- A Function Representation for Learning in Banach Spaces -- Local Complexities for Empirical Risk Minimization -- Model Selection by Bootstrap Penalization for Classification -- MDL -- Convergence of Discrete MDL for Sequential Prediction -- On the Convergence of MDL Density Estimation -- Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification -- Generalisation I -- Learning Intersections of Halfspaces with a Margin -- A General Convergence Theorem for the Decomposition Method -- Generalisation II -- Oracle Bounds and Exact Algorithm for Dyadic Classification Trees -- An Improved VC Dimension Bound for Sparse Polynomials -- A New PAC Bound for Intersection-Closed Concept Classes -- Clustering and Distributed Learning -- A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering -- Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers -- Consistency in Models for Communication Constrained Distributed Learning -- On the Convergence of Spectral Clustering on Random Samples: The Normalized Case -- Boosting -- Performance Guarantees for Regularized Maximum Entropy Density Estimation -- Learning Monotonic Linear Functions -- Boosting Based on a Smooth Margin -- Kernels and Probabilities -- Bayesian Networks and Inner Product Spaces -- An Inequality for Nearly Log-Concave Distributions with Applications to Learning -- Bayes and Tukey Meet at the Center Point -- Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results -- Kernels and Kernel Matrices -- A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra -- Statistical Properties of Kernel Principal Component Analysis -- Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA -- Regularization and Semi-supervised Learning on Large Graphs -- Open Problems -- Perceptron-Like Performance for Intersections of Halfspaces -- The Optimal PAC Algorithm -- The Budgeted Multi-armed Bandit Problem. 410 0$aLecture Notes in Artificial Intelligence ;$v3120 606 $aArtificial intelligence 606 $aMathematical logic 606 $aAlgorithms 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 615 0$aArtificial intelligence. 615 0$aMathematical logic. 615 0$aAlgorithms. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aMathematical Logic and Formal Languages. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aComputation by Abstract Devices. 676 $a006.31 702 $aShawe-Taylor$b John$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSinger$b Yoram$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910767544303321 996 $aLearning Theory$9772233 997 $aUNINA