Learning theory : 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004 : proceedings

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Bibliografiset tiedot
Yhteisötekijä: Annual Conference on Learning Theory :Banff, CA
Muut tekijät: Shawe-Taylor, John (Päätoimittaja), Singer, Yoram (Päätoimittaja)
Aineistotyyppi: Livre numérique
Kieli:Anglais
Julkaistu: Berlin [etc.] : Springer [20..].
Cham : Springer Nature
Sarja:Lecture notes in computer science. Lecture notes in artificial intelligence 3120
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Linkit:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
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Huomautus: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Learning theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, proceedings, John Shawe-Taylor, Yoram Singer (eds.), Berlin, Springer, 2004, 1 vol. (X-645 p.), Lecture notes in computer science, 3-540-22282-0
• Learning Theory, Texte imprimé, 9783662204115
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260 |a Berlin [etc.] :  |b Springer. 
260 |a Cham :  |b Springer Nature,  |c [20..]. 
490 0 |a Lecture notes in computer science. Lecture notes in artificial intelligence  |v 3120  |x 1611-3349  |x 2945-9141 
500 |a Archives Springer e-books (Licence nationale) 
500 |a Archives Springer e-books (Licence nationale) 
505 0 |a Economics 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 forIntersection-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. 
506 |a Accès en ligne pour les établissements français bénéficiaires des licences nationales 
506 |a Accès soumis à abonnement pour tout autre établissement 
506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
650 |a Informatique 
650 |a Apprentissage automatique 
650 |a Algorithmes 
650 |a Intelligence artificielle 
650 |a Logique symbolique et mathématique 
650 |a Actes de congrès 
700 1 |a Shawe-Taylor, John.  |4 pbd 
700 1 |a Singer, Yoram.  |4 pbd 
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