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dc.contributor.authorQuiñonero-Candela, Joaquinen_US
dc.contributor.editorDagan, Idoen_US
dc.contributor.editorMagnini, Bernardoen_US
dc.contributor.editord’Alché-Buc, Florenceen_US
dc.date.accessioned2019-03-26T08:29:38Z
dc.date.available2019-03-26T08:29:38Z
dc.date.issued2006en_US
dc.identifier.isbn3540334270en_US
dc.identifier.isbn9783540334279en_US
dc.identifier.otherHPU1161231en_US
dc.identifier.urihttps://lib.hpu.edu.vn/handle/123456789/32304
dc.description.abstractThis book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005.The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theoryen_US
dc.description.abstractthe second challenge was to recognize objects from a number of visual object classes in realistic scenesen_US
dc.description.abstractthe third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.en_US
dc.format.extent473 p.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoenen_US
dc.publisherSpringer-Verlag Berlin Heidelbergen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectTechnologyen_US
dc.subjectComputeren_US
dc.subjectlgorithm Analysisen_US
dc.subjectProblem Complexityen_US
dc.titleMachine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papersen_US
dc.typeBooken_US
dc.size5,457 KBen_US
dc.departmentTechnologyen_US


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