机器学习介绍(英文版：备注里有中文翻译)PPT课件

MachineLearning,2020年5月19日星期二,1,.,Machinelearning,asabranchofartificialintelligence,isgeneraltermsofakindofanalyticalmethod.Itmainlyutilizescomputersimulateorrealizethelearnedbehaviorofhuman.,2020年5月19日星期二,2,.,2020年5月19日星期二,1）Machinelearningjustlikeatruechampionwhichgohaughtily;2）Patternrecognitioninprocessofdeclineanddieout;3）Deeplearningisabrand-newandrapidlyrisingfield.,theGooglesearchindexofthreeconceptsince2004,3,.,2020年5月19日星期二,Theconstructedmachinelearningsystembasedoncomputermainlycontainstwocoreparts：representationandgeneralization.Thefirststepfordatalearningistorepresentthedata,i.e.detectthepatternofdata.Establishageneralizedmodelofdataspaceaccordingtoagroupofknowndatatopredictthenewdata.Thecoretargetofmachinelearningistogeneralizefromknownexperience.Generalizationmeansapowerofwhichthemachinelearningsystemtobelearnedforknowndatathatcouldpredictthenewdata.,4,.,SupervisedlearningInputdatahaslabels.Thecommonkindoflearningalgorithmisclassification.Themodelhasbeentrainedviathecorrespondencebetweenfeatureandlabelofinputdata.Therefore,whensomeunknowndatawhichhasfeaturesbutnolabelinput,wecanpredictthelabelofunknowndataaccordingtotheexistingmodel.,2020年5月19日星期二,5,.,UnsupervisedlearningInputdatahasnolabels.Itrelatestoanotherlearningalgorithm,i.e.clustering.Thebasicdefinitionisacoursethatdividethegatherofphysicalorabstractobjectintomultipleclasswhichconsistofsimilarobjects.,2020年5月19日星期二,6,.,Iftheoutputeigenvectormarkscomefromalimitedsetthatconsistofclassornamevariable,thenthekindofmachinelearningbelongstoclassificationproblem.Ifoutputmarkisacontinuousvariable,thenthekindofmachinelearningbelongstoregressionproblem.,2020年5月19日星期二,7,.,Classificationstep,Featureextraction,Featureselection,Modeltraining,Classificationandprediction,Rawdata,Newdata,2020年5月19日星期二,8,.,Featureselection（featurereduction）,CurseofDimensionality：Usuallyrefertotheproblemthatconcernedaboutcomputationofvector.Withtheincreaseofdimension,calculatedamountwilljumpexponentially.Corticalfeaturesofdifferentbrainregionsexhibitvarianteffectduringtheclassificationprocessandmayexistsomeredundantfeature.Inparticularafterthemultimodalfusion,theincreaseoffeaturedimensionwillcause“curseofDimensionality”.,2020年5月19日星期二,9,.,PrincipalComponentAnalysis,PCAPCAisthemostcommonlineardimensionreductionmethod.Itstargetismappingthedataofhighdimensiontolow-dimensionspaceviacertainlinearprojection,andexpectthevarianceofdatathatprojectthecorrespondingdimensionismaximum.Itcanusefewerdatadimensionmeanwhileretainthemajorcharacteristicofrawdata.,2020年5月19日星期二,10,.,Lineardiscriminantanalysis,LDAThebasicideaofLDAisprojection,mappingtheNdimensiondatatolow-dimensionspaceandseparatethebetween-groupsassoonaspossible.i.e.theoptimalseparabilityinthespace.Thebenchmarkisthenewsubspacehasmaximumbetweenclassdistanceandminimalinter-objectdistance.,2020年5月19日星期二,11,.,Independentcomponentanalysis,ICAThebasicideaofICAistoextracttheindependencesignalfromagroupofmixedobservedsignaloruseindependencesignaltorepresentothersignal.,2020年5月19日星期二,12,.,Recursivefeatureeliminationalgorithm,RFERFEisagreedyalgorithmthatwipeoffinsignificancefeaturestepbysteptoselectthefeature.Firstly,cyclicorderingthefeatureaccordingtotheweightofsub-featureinclassificationandremovethefeaturewhichrankatterminalonebyone.Then,accordingtothefinalfeatureorderinglist,selectdifferentdimensionofseveralfeaturesubsetfronttoback.Assesstheclassificationeffectofdifferentfeaturesubsetandthengettheoptimalfeaturesubset.,2020年5月19日星期二,13,.,Classificationalgorithm,DecisiontreeDecisiontreeisatreestructure.Eachnonleafnodeexpressesthetestofafeaturepropertyandeachbranchexpressestheoutputoffeaturepropertyincertainrangeandeachleafnodestoresaclass.Thedecision-makingcourseofdecisiontreeisstartingfromrootnode,testingthecorrespondingfeaturepropertyofwaitingobjects,selectingtheoutputbranchaccordingtotheirvalues,untilreachingtheleafnodeandtaketheclassthatleafnodestoreasthedecisionresult.,2020年5月19日星期二,14,.,NaiveBayes,NBNBclassificationalgorithmisaclassificationmethodinstatistics.Ituseprobabilitystatisticsknowledgeforclassification.Thisalgorithmcouldapplytolargedatabaseandithashighclassificationaccuracyandhighspeed.,2020年5月19日星期二,15,.,Artificialneuralnetwork,ANNANNisamathematicalmodelthatapplyakindofstructurewhichsimilarwithsynapseconnectionforinformationprocessing.Inthismodel,amassofnodeformanetwork,i.e.neuralnetwork,toreachthegoalofinformationprocessing.Neuralnetworkusuallyneedtotrain.Thecourseoftrainingisnetworklearning.Thetrainingchangethelinkweightofnetworknodeandmakeitpossessthefunctionofclassification.Thenetworkaftertrainingapplytorecognizeobject.,2020年5月19日星期二,16,.,k-NearestNeighbors,kNNkNNalgorithmisakindofclassificationmethodbaseonlivingexample.Thismethodistofindthenearestktrainingsampleswithunknownsamplexandexaminethemostofksamplesbelongtowhichclass,thenxbelongstothatclass.kNNisalazylearningmethod.Itstoressamplesbutproceedclassificationuntilneedtoclassify.Ifsamplesetarerelativelycomplex,itmaybeleadtolargecomputationoverhead.Soitcannotapplytostronglyreal-timeoccasion.,2020年5月19日星期二,17,.,supportvectormachine,SVMMappingthelinearlyinseparabledatainlow-dimensionspacetohigh-dimensionspaceandmakeitlinearlyseparable,2020年5月19日星期二,18,.,Crossvalidation,CV,ThebasicideaofCVisgroupingtherawdatainasense.Onepartistakenastrainset,theotherpartistakenasvalidationset.Primarily,theclassifieristrainedwithtrainset,andthenusevalidationsettotestthereceivedmodelbytraining.,2020年5月19日星期二,19,.,K-foldcross-validationInk-foldcross-validation,theoriginalsampleisrandomlypartitionedintokequalsizedsubsamples.Oftheksubsamples,asinglesubsampleisretainedasthevalidationdatafortestingthemodel,andtheremainingk1subsamplesareusedastrainingdata.Thecross-validationprocessisthenrepeatedktimes(thefolds),witheachoftheksubsamplesusedexactlyonceasthevalidationdata.Thekresultsfromthefoldscanthenbeaveragedtoproduceasingleestimation.Theadvantageofthismethodoverrepeatedrandomsub-samplingisthatallobservationsareusedforbothtrainingandvalidation,andeachobservationisusedforvalidationexactlyonce.10-foldcross-validationiscommonlyused.,2020年5月19日星期二,20,.,Leave-one-outcross-validation,LOOCVWhenk=n(thenumberofobservations),thek-foldcross-validationisexactlytheleave-one-outcross-validation.,2020年5月19日星期二,21,.,confusionmatrix,TPgoldstandardandtestaffirmsufferfromcertainillness;TNgoldstandardandtest