机器学习介绍英文版备注里有中文翻译

MachineLearning07一月2023Machinelearning,asabranchofartificialintelligence,isgeneraltermsofakindofanalyticalmethod.Itmainlyutilizesputersimulateorrealizethelearnedbehaviorofhuman.07一月202307一月20231）Machinelearningjustlikeatruechampionwhichgohaughtily;

2）Patternrecognitioninprocessofdeclineanddieout;

3）Deeplearningisabrand-newandrapidlyrisingfield.theGooglesearchindexofthreeconceptsince202307一月2023Theconstructedmachinelearningsystembasedonputermainlycontainstwocoreparts：representationandgeneralization.Thefirststepfordatalearningistorepresentthedata,i.e.detectthepatternofdata.Establishageneralizedmodelofdataspaceaccordingtoagroupofknowndatatopredictthenewdata.Thecoretargetofmachinelearningistogeneralizefromknownexperience.Generalizationmeansapowerofwhichthemachinelearningsystemtobelearnedforknowndatathatcouldpredictthenewdata.SupervisedlearningInputdatahaslabels.Themonkindoflearningalgorithmisclassification.Themodelhasbeentrainedviathecorrespondencebetweenfeatureandlabelofinputdata.Therefore,whensomeunknowndatawhichhasfeaturesbutnolabelinput,wecanpredictthelabelofunknowndataaccordingtotheexistingmodel.07一月2023UnsupervisedlearningInputdatahasnolabels.Itrelatestoanotherlearningalgorithm,i.e.clustering.Thebasicdefinitionisacoursethatdividethegatherofphysicalorabstractobjectintomultipleclasswhichconsistofsimilarobjects.07一月2023Iftheoutputeigenvectormarksefromalimitedsetthatconsistofclassornamevariable,thenthekindofmachinelearningbelongstoclassificationproblem.Ifoutputmarkisacontinuousvariable,thenthekindofmachinelearningbelongstoregressionproblem.07一月2023ClassificationstepFeatureextractionFeatureselectionModeltrainingClassificationandpredictionRawdataNewdata07一月2023Featureselection（featurereduction）CurseofDimensionality：Usuallyrefertotheproblemthatconcernedaboutputationofvector.Withtheincreaseofdimension,calculatedamountwilljumpexponentially.Corticalfeaturesofdifferentbrainregionsexhibitvarianteffectduringtheclassificationprocessandmayexistsomeredundantfeature.Inparticularafterthemultimodalfusion,theincreaseoffeaturedimensionwillcause“curseofDimensionality”.07一月2023PrincipalComponentAnalysis,PCAPCAisthemostmonlineardimensionreductionmethod.Itstargetismappingthedataofhighdimensiontolow-dimensionspaceviacertainlinearprojection,andexpectthevarianceofdatathatprojectthecorrespondingdimensionismaximum.Itcanusefewerdatadimensionmeanwhileretainthemajorcharacteristicofrawdata.07一月2023Lineardiscriminantanalysis,LDAThebasicideaofLDAisprojection,mappingtheNdimensiondatatolow-dimensionspaceandseparatethebetween-groupsassoonaspossible.i.e.theoptimalseparabilityinthespace.Thebenchmarkisthenewsubspacehasmaximumbetweenclassdistanceandminimalinter-objectdistance.07一月2023Independentponentanalysis,ICAThebasicideaofICAistoextracttheindependencesignalfromagroupofmixedobservedsignaloruseindependencesignaltorepresentothersignal.07一月2023Recursivefeatureeliminationalgorithm,RFERFEisagreedyalgorithmthatwipeoffinsignificancefeaturestepbysteptoselectthefeature.Firstly,cyclicorderingthefeatureaccordingtotheweightofsub-featureinclassificationandremovethefeaturewhichrankatterminalonebyone.Then,accordingtothefinalfeatureorderinglist,selectdifferentdimensionofseveralfeaturesubsetfronttoback.Assesstheclassificationeffectofdifferentfeaturesubsetandthengettheoptimalfeaturesubset.

07一月2023Classificationalgorithm

DecisiontreeDecisiontreeisatreestructure.Eachnonleafnodeexpressesthetestofafeaturepropertyandeachbranchexpressestheoutputoffeaturepropertyincertainrangeandeachleafnodestoresaclass.Thedecision-makingcourseofdecisiontreeisstartingfromrootnode,testingthecorrespondingfeaturepropertyofwaitingobjects,selectingtheoutputbranchaccordingtotheirvalues,untilreachingtheleafnodeandtaketheclassthatleafnodestoreasthedecisionresult.07一月2023NaiveBayes,NBNBclassificationalgorithmisaclassificationmethodinstatistics.Ituseprobabilitystatisticsknowledgeforclassification.Thisalgorithmcouldapplytolargedatabaseandithashighclassificationaccuracyandhighspeed.07一月2023Artificialneuralnetwork,ANNANNisamathematicalmodelthatapplyakindofstructurewhichsimilarwithsynapseconnectionforinformationprocessing.Inthismodel,amassofnodeformanetwork,i.e.neuralnetwork,toreachthegoalofinformationprocessing.Neuralnetworkusuallyneedtotrain.Thecourseoftrainingisnetworklearning.Thetrainingchangethelinkweightofnetworknodeandmakeitpossessthefunctionofclassification.Thenetworkaftertrainingapplytorecognizeobject.07一月2023k-NearestNeighbors,kNNkNNalgorithmisakindofclassificationmethodbaseonlivingexample.Thismethodistofindthenearestktrainingsampleswithunknownsamplexandexaminethemostofksamplesbelongtowhichclass,thenxbelongstothatclass.kNNisalazylearningmethod.Itstoressamplesbutproceedclassificationuntilneedtoclassify.Ifsamplesetarerelativelyplex,itmaybeleadtolargeputationoverhead.Soitcannotapplytostronglyreal-timeoccasion.07一月2023supportvectormachine,SVMMappingthelinearlyinseparabledatainlow-dimensionspacetohigh-dimensionspaceandmakeitlinearlyseparable07一月2023Crossvalidation,CVThebasicideaofCVisgroupingtherawdatainasense.Onepartistakenastrainset,theotherpartistakenasvalidationset.Primarily,theclassifieristrainedwithtrainset,andthenusevalidationsettotestthereceivedmodelbytraining.07一月2023K-foldcross-validationIn

k-foldcross-validation,theoriginalsampleisrandomlypartitionedinto

k

equalsizedsubsamples.Ofthe

k

subsamples,asinglesubsampleisretainedasthevalidationdatafortestingthemodel,andtheremaining

k

−

1subsamplesareusedastrainingdata.Thecross-validationprocessisthenrepeated

k

times(the

folds),witheachofthe

k

subsamplesusedexactlyonceasthevalidationdata.The

k

resultsfromthefoldscanthenbeaveragedtoproduceasingleestimation.Theadvantageofthismethodoverrepeatedrandomsub-samplingisthatallobservationsareusedforbothtrainingandvalidation,andeachobservationisusedforvalidationexactlyonce.10-foldcross-validationismonlyused.07一月2023Leave-one-outcross-validation,LOOCVWhen

k

=

n

(thenumberofobservations),the

k-foldcross-validationisexactlytheleave-one-outcross-validation.07一月2023confusionmatrixTP——goldstandardandtestaffirmsufferfromcertainillness;TN——goldstan