Learning theory and Kernel machines : 16th annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003 : proceedings
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| Enti autori: | , |
|---|---|
| Altri autori: | , |
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serie: | Lecture notes in computer science. Lecture notes in artificial intelligence
2777 |
| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
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 and Kernel machines, 16th annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, proceedings, Bernhard Schölkopf, Manfred K. Warmuth (eds.), Berlin, Springer, 2003, 1 vol. (XIV-746 p.), Lecture notes in computer science, 3-540-40720-0 • Learning Theory and Kernel Machines, Texte imprimé, 9783662164792 |
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| 111 | 2 | |a Annual Conference on Learning Theory |n (16 |d :2003 |c :Washington, DC, USA). | |
| 245 | 1 | 0 | |a Learning theory and Kernel machines : |b 16th annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003 : proceedings |c [edited by] Bernhard Schölkopf, Manfred K. Warmuth. |
| 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 2777 |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 Target Area: Computational Game Theory -- Tutorial: Learning Topics in Game-Theoretic Decision Making -- A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria -- Preference Elicitation and Query Learning -- Efficient Algorithms for Online Decision Problems -- Positive Definite Rational Kernels -- Bhattacharyya and Expected Likelihood Kernels -- Maximal Margin Classification for Metric Spaces -- Maximum Margin Algorithms with Boolean Kernels -- Knowledge-Based Nonlinear Kernel Classifiers -- Fast Kernels for Inexact String Matching -- On Graph Kernels: Hardness Results and Efficient Alternatives -- Kernels and Regularization on Graphs -- Data-Dependent Bounds for Multi-category Classification Based on Convex Losses -- Poster Session 1 -- Comparing Clusterings by the Variation of Information -- Multiplicative Updates for Large Margin Classifiers -- Simplified PAC-Bayesian Margin Bounds -- Sparse Kernel Partial Least Squares Regression -- Sparse Probability Regression by Label Partitioning -- Learning with Rigorous Support Vector Machines -- Robust Regression by Boosting the Median -- Boosting with Diverse Base Classifiers -- Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming -- Optimal Rates of Aggregation -- Distance-Based Classification with Lipschitz Functions -- Random Subclass Bounds -- PAC-MDL Bounds -- Universal Well-Calibrated Algorithm for On-Line Classification -- Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling -- Learning Algorithms for Enclosing Points in Bregmanian Spheres -- Internal Regret in On-Line Portfolio Selection -- Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem -- Smooth ?-Insensitive Regression by Loss Symmetrization -- On Finding Large Conjunctive Clusters -- LearningArithmetic Circuits via Partial Derivatives -- Poster Session 2 -- Using a Linear Fit to Determine Monotonicity Directions -- Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering -- Sequence Prediction Based on Monotone Complexity -- How Many Strings Are Easy to Predict? -- Polynomial Certificates for Propositional Classes -- On-Line Learning with Imperfect Monitoring -- Exploiting Task Relatedness for Multiple Task Learning -- Approximate Equivalence of Markov Decision Processes -- An Information Theoretic Tradeoff between Complexity and Accuracy -- Learning Random Log-Depth Decision Trees under the Uniform Distribution -- Projective DNF Formulae and Their Revision -- Learning with Equivalence Constraints and the Relation to Multiclass Learning -- Target Area: Natural Language Processing -- Tutorial: Machine Learning Methods in Natural Language Processing -- Learning from Uncertain Data -- Learning and Parsing Stochastic Unification-Based Grammars -- Generality s Price -- On Learning to Coordinate -- Learning All Subfunctions of a Function -- When Is Small Beautiful? -- Learning a Function of r Relevant Variables -- Subspace Detection: A Robust Statistics Formulation -- How Fast Is k-Means? -- Universal Coding of Zipf Distributions -- An Open Problem Regarding the Convergence of Universal A Priori Probability -- Entropy Bounds for Restricted Convex Hulls -- Compressing to VC Dimension Many Points. | |
| 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 Schölkopf, Bernhard, |d 19..- |4 pbd | |
| 700 | 1 | |a Warmuth, Manfred K., |d 19..- |4 pbd | |
| 711 | 2 | |a Annual Workshop on Kernel Machines |n (7 |d :2003 |c :Washington, DC, USA). |4 aut | |
| 776 | 0 | |0 074295942 |t Learning theory and Kernel machines |o 16th annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003 |o proceedings |f Bernhard Schölkopf, Manfred K. Warmuth (eds.) |c Berlin |n Springer |d 2003 |p 1 vol. (XIV-746 p.) |s Lecture notes in computer science |z 3-540-40720-0 | |
| 776 | 0 | |t Learning Theory and Kernel Machines |b Texte imprimé |z 9783662164792 | |
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| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-ZJZ8NXP5-H |z Accès sur la plateforme Istex | |
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