Computational learning theory : 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002 : proceedings
著者
書誌事項
Computational learning theory : 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002 : proceedings
（Lecture notes in computer science, 2375 . Lecture notes in artificial intelligence）
Springer, c2002
大学図書館所蔵 全34件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
ThisvolumecontainspaperspresentedattheFifteenthAnnualConferenceon ComputationalLearningTheory(COLT2002)heldonthemaincampusofthe UniversityofNewSouthWalesinSydney,AustraliafromJuly8to10,2002. Naturally,thesearepapersinthe?eldofcomputationallearningtheory,a- search?elddevotedtostudyingthedesignandanalysisofalgorithmsformaking predictionsaboutthefuturebasedonpastexperiences,withanemphasisonr- orousmathematicalanalysis. COLT2002wasco-locatedwiththeNineteenthInternationalConferenceon MachineLearning(ICML2002)andwiththeTwelfthInternationalConference onInductiveLogicProgramming(ILP2002). NotethatCOLT2002wasthe?rstconferencetotakeplaceafterthefull mergeroftheAnnualConferenceonComputationalLearningTheorywiththe EuropeanConferenceonComputationalLearningTheory. (In2001ajointc- ferenceconsistingofthe5thEuropeanConferenceonComputationalLearning Theoryandthe14thAnnualConferenceonComputationalLearningTheory washeld;thelastindependentEuropeanConferenceonComputationalLea- ingTheorywasheldin1999. ) ThetechnicalprogramofCOLT2002contained26papersselectedfrom 55submissions.
Inaddition,ChristosPapadimitriou(UniversityofCaliforniaat Berkeley)wasinvitedtogiveakeynotelectureandtocontributeanabstractof hislecturetotheseproceedings. TheMarkFulkAwardispresentedannuallyforthebestpapercoauthored byastudent. Thisyear'sawardwaswonbySandraZillesforthepaper"Merging UniformInductiveLearners. " April2002 JyrkiKivinen RobertH. Sloan Thanks and Acknowledgments Wegratefullythankalltheindividualsandorganizationsresponsibleforthe successoftheconference. ProgramCommittee Weespeciallywanttothanktheprogramcommittee:DanaAngluin(Yale), JavedAslam(Dartmouth),PeterBartlett(BIOwulfTechnologies),ShaiBen- David(Technion),JohnCase(Univ. ofDelaware),PeterGru..nwald(CWI),Ralf 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 MattiKa...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 ...1
69 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...
目次
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.
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