Computational Learning Theory

15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings
2002
ISBN: 978-3-540-43836-6
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 2375, 412 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1310 g

Reihe: Lecture Notes in Computer Science

Herbrich(MicrosoftResearch),MarkHerbster(UniversityCollegeLondon), G´aborLugosi(PompeuFabraUniversity),RonMeir(Technion),ShaharMend- son(AustralianNationalUniv. ),MichaelSchmitt(Ruhr-Universit¨atBochum), RoccoServedio(Harvard),andSantoshVempala(MIT). WealsoacknowledgethecreatorsoftheCyberChairsoftwareformakinga softwarepackagethathelpedthecommitteedoitswork. Local Arrangements, Co-located Conferences Support SpecialthanksgotoourconferencechairArunSharmaandlocalarrangements chairEricMartin(bothatUniv. ofNewSouthWales)forsettingupCOLT2002 inSydney. RochelleMcDonaldandSueLewisprovidedadministrativesupport. ClaudeSammutinhisroleasconferencechairofICMLandprogramco-chair ofILPensuredsmoothcoordinationwiththetwoco-locatedconferences. COLT Community ForkeepingtheCOLTseriesgoing,wethanktheCOLTsteeringcommittee, andespeciallyChairJohnShawe-TaylorandTreasurerJohnCaseforalltheir hardwork. WealsothankStephenKwekformaintainingtheCOLTwebsiteat http://www. learningtheory. org. Sponsoring Institution SchoolofComputerScienceandEngineering,UniversityofNewSouthWales, Australia VIII Thanks and Acknowledgments Referees PeterAuer LisaHellerstein AlainPajor AndrewBarto DanielHerrmann GunnarR¨atsch StephaneBoucheron ColindelaHiguera RobertSchapire OlivierBousquet SeanHolden JohnShawe-Taylor Nicol`oCesa-Bianchi MarcusHutter TakeshiShinohara TapioElomaa SanjayJain DavidShmoys RanEl-Yaniv YuriKalnishkan YoramSinger AllanErskine MakotoKanazawa CarlSmith HenningFernau SatoshiKobayashi FrankStephan J¨urgenForster VladimirKoltchinskii Gy¨orgyTur´an DeanFoster MattiKä ¨ariai ¨nen PaulVitan ´yi ClaudioGentile WeeSunLee ManfredWarmuth JudyGoldsmith ShieMannor JonA. Wellner ThoreGraepel RyanO’Donnell RobertC. Williamson Table of Contents Statistical Learning Theory AgnosticLearningNonconvexFunctionClasses. 1 Shahar Mendelson andRobertC. Williamson Entropy,CombinatorialDimensionsandRandomAverages. 14 Shahar Mendelson andRoman Vershynin GeometricParametersofKernelMachines. 29 Shahar Mendelson LocalizedRademacherComplexities. 44 PeterL. Bartlett,Olivier Bousquet,and Shahar Mendelson SomeLocalMeasuresofComplexityofConvexHulls andGeneralizationBounds. 59 Olivier Bousquet,Vladimir Koltchinskii, and DmitriyPanchenko OnlineLearning PathKernelsandMultiplicativeUpdates. 74 Eiji Takimoto andManfred K. Warmuth PredictiveComplexityandInformation. 90 Michael V. Vyugin andVladimir V. V’yugin MixabilityandtheExistenceofWeakComplexities. 105 YuriKalnishkan andMichael V. Vyugin ASecond-OrderPerceptronAlgorithm. 121 Nicolo ` Cesa-Bianchi, AlexConconi, and Claudio Gentile TrackingLinear-ThresholdConceptswithWinnow. 138 Chris Mesterharm Inductive Inference LearningTreeLanguagesfromText. 153 HenningFernau PolynomialTimeInductiveInferenceofOrderedTreePatterns withInternalStructuredVariablesfromPositiveData. 169 YusukeSuzuki,RyutaAkanuma,Takayoshi Shoudai, TetsuhiroMiyahara, andTomoyuki Uchida X Table of Contents InferringDeterministicLinearLanguages. 185 Colin dela HigueraandJoseOncina MergingUniformInductiveLearners. 201 SandraZilles TheSpeedPrior:ANewSimplicityMeasure YieldingNear-OptimalComputablePredictions. 216 J¨ urgenSchmidhuber PAC Learning NewLowerBoundsforStatisticalQueryLearning. 229 KeYang ExploringLearnabilitybetweenExactandPAC. 244 Nader H. Bshouty, Je?reyC. Jackson, andChristino Tamon PACBoundsforMulti-armedBanditandMarkovDecisionProcesses.

Zielgruppe

Research

Autoren/Hrsg.

Sloan, Robert H.

Kivinen, Jyrki

Fachgebiete

• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein E-Learning
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Statistical 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.