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PAGE\*MERGEFORMAT12|SVMTutorialTutorialonSupportVectorMachine(SVM)VikramadityaJakkula,SchoolofEECS,WashingtonStateUniversity,Pullman99164.Abstract:InthistutorialwepresentabriefintroductiontoSVM,andwediscussaboutSVMfrompublishedpapers,workshopmaterials&materialcollectedfrombooksandmaterialavailableonlineontheWorldWideWeb.InthebeginningwetrytodefineSVMandtrytotalkaswhySVM,withabriefoverviewofstatisticallearningtheory.ThemathematicalformulationofSVMispresented,andtheoryfortheimplementationofSVMisbrieflydiscussed.FinallysomeconclusionsonSVMandapplicationareasareincluded.SupportVectorMachines(SVMs)arecompetingwithNeuralNetworksastoolsforsolvingpatternrecognitionproblems.ThistutorialassumesyouarefamiliarwithconceptsofLinearAlgebra,realanalysisandalsounderstandtheworkingofneuralnetworksandhavesomebackgroundinAI.IntroductionMachineLearningisconsideredasasubfieldofArtificialIntelligenceanditisconcernedwiththedevelopmentoftechniquesandmethodswhichenablethecomputertolearn.Insimpletermsdevelopmentofalgorithmswhichenablethemachinetolearnandperformtasksandactivities.Machinelearningoverlapswithstatisticsinmanyways.Overtheperiodoftimemanytechniquesandmethodologiesweredevelopedformachinelearningtasks[1].SupportVectorMachine(SVM)wasfirstheardin1992,introducedbyBoser,Guyon,andVapnikinCOLT-92.Supportvectormachines(SVMs)areasetofrelatedsupervisedlearningmethodsusedforclassificationandregression[1].Theybelongtoafamilyofgeneralizedlinearclassifiers.Inanotherterms,SupportVectorMachine(SVM)isaclassificationandregressionpredictiontoolthatusesmachinelearningtheorytomaximizepredictiveaccuracywhileautomaticallyavoidingover-fittothedata.SupportVectormachinescanbedefinedassystemswhichusehypothesisspaceofalinearfunctionsinahighdimensionalfeaturespace,trainedwithalearningalgorithmfromoptimizationtheorythatimplementsalearningbiasderivedfromstatisticallearningtheory.SupportvectormachinewasinitiallypopularwiththeNIPScommunityandnowisanactivepartofthemachinelearningresearcharoundthewor
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