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020 _a9783540454359
_9978-3-540-45435-9
024 7 _a10.1007/3-540-45435-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aComputational Learning Theory
_h[electronic resource] :
_b15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings /
_cedited by Jyrki Kivinen, Robert H. Sloan.
250 _a1st ed. 2002.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2002.
300 _aXII, 412 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2375
505 0 _aStatistical Learning Theory -- Agnostic Learning Nonconvex Function Classes -- Entropy, Combinatorial Dimensions and Random Averages -- Geometric Parameters of Kernel Machines -- Localized Rademacher Complexities -- Some Local Measures of Complexity of Convex Hulls and Generalization Bounds -- Online Learning -- Path Kernels and Multiplicative Updates -- Predictive Complexity and Information -- Mixability and the Existence of Weak Complexities -- A Second-Order Perceptron Algorithm -- Tracking Linear-Threshold Concepts with Winnow -- Inductive Inference -- Learning Tree Languages from Text -- Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data -- Inferring Deterministic Linear Languages -- Merging Uniform Inductive Learners -- The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions -- PAC Learning -- New Lower Bounds for Statistical Query Learning -- Exploring Learnability between Exact and PAC -- PAC Bounds for Multi-armed Bandit and Markov Decision Processes -- Bounds for the Minimum Disagreement Problem with Applications to Learning Theory -- On the Proper Learning of Axis Parallel Concepts -- Boosting -- A Consistent Strategy for Boosting Algorithms -- The Consistency of Greedy Algorithms for Classification -- Maximizing the Margin with Boosting -- Other Learning Paradigms -- Performance Guarantees for Hierarchical Clustering -- Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures -- Prediction and Dimension -- Invited Talk -- Learning the Internet.
650 0 _aArtificial intelligence.
650 0 _aMachine theory.
650 0 _aComputer science.
650 0 _aAlgorithms.
650 1 4 _aArtificial Intelligence.
650 2 4 _aFormal Languages and Automata Theory.
650 2 4 _aTheory of Computation.
650 2 4 _aAlgorithms.
700 1 _aKivinen, Jyrki.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSloan, Robert H.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540438366
776 0 8 _iPrinted edition:
_z9783662167175
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2375
856 4 0 _uhttps://doi.org/10.1007/3-540-45435-7
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