台大-李宏毅-B站机器学习视频-课件神经网络与深度学习Classification

Classification:ProbabilisticGenerativeModel,Classification,CreditScoringInput:income,savings,profession,age,pastfinancialhistoryOutput:acceptorrefuseMedicalDiagnosisInput:currentsymptoms,age,gender,pastmedicalhistoryOutput:whichkindofdiseasesHandwrittencharacterrecognitionFacerecognitionInput:imageofaface,output:person,金,Input:,output:,Function,Classn,ExampleApplication,=,=,=,ExampleApplication,Total:sumofallstatsthatcomeafterthis,ageneralguidetohowstrongapokemonisHP:hitpoints,orhealth,defineshowmuchdamageapokemoncanwithstandbeforefaintingAttack:thebasemodifierfornormalattacks(eg.Scratch,Punch)Defense:thebasedamageresistanceagainstnormalattacksSPAtk:specialattack,thebasemodifierforspecialattacks(e.g.fireblast,bubblebeam)SPDef:thebasedamageresistanceagainstspecialattacksSpeed:determineswhichpokemonattacksfirsteachround,Canwepredictthe“type”ofpokemonbasedontheinformation?,320,35,55,40,50,50,90,pokemongames(NOTpokemoncardsorPokemonGo),ExampleApplication,HowtodoClassification,TrainingdataforClassification,1,1,2,2,Training:Class1meansthetargetis1;Class2meansthetargetis-1,ClassificationasRegression?,Binaryclassificationasexample,Testing:closerto1class1;closerto-1class2,Multipleclass:Class1meansthetargetis1;Class2meansthetargetis2;Class3meansthetargetis3problematic,Penalizetotheexamplesthatare“toocorrect”,(Bishop,P186),Class1,x1,x1,x2,x2,Class2,Class1,Class2,1,1,-1,-1,y=b+w1x1+w2x2,b+w1x1+w1x1=0,1,error,todecreaseerror,IdealAlternatives,Function(Model):Lossfunction:Findthebestfunction:Example:Perceptron,SVM,NotToday,Output=class1,0,=,Output=class2,Thenumberoftimesfgetincorrectresultsontrainingdata.,TwoBoxes,Box1,Box2,P(B1)=2/3,P(B2)=1/3,P(Blue|B1)=4/5,P(Green|B1)=1/5,P(Blue|B1)=2/5,P(Green|B1)=3/5,Wheredoesitcomefrom?,P(B1|Blue),=Blue|11Blue|11+Blue|22,TwoClasses,Givenanx,whichclassdoesitbelongto,=|11|11+|22,1|,=|11+|22,GenerativeModel,P(C1),P(C2),P(x|C1),P(x|C2),EstimatingtheProbabilitiesFromtrainingdata,Class1,Class2,Prior,Class1,Class2,P(C1),P(C2),Water,Normal,WaterandNormaltypewithID0.5,xbelongstoclass1(Water),P(C1)=79/(79+61)=0.56,P(C2)=61/(79+61)=0.44,1,1=12/2111/2121111,1=75.071.3,2=55.659.8,1=874327327929,2=847422422685,2,2=12/2121/2122212,Howstheresults?,Testingdata:47%accuracy,All:total,hp,att,spatt,de,spde,speed(7features),54%accuracy,1|,Bluepoints:C1(Water),Redpoints:C2(Normal),1|0.5,1|0.5,1|0.5,output:class1,Otherwise,output:class2,ProbabilityDistribution,Youcanalwaysusethedistributionyoulike,|1,Ifyouassumeallthedimensionsareindependent,thenyouareusingNaiveBayesClassifier.,12,=1|1,2|1,|1,1-DGaussian,Forbinaryfeatures,youmayassumetheyarefromBernoullidistributions.,PosteriorProbability,1|=|11|11+|22,=11+|22|11,=11+,=|11|22,=,Sigmoidfunction,WarningofMath,PosteriorProbability,1|,=,=|11|22,|1=12/2111/2121111,|2=12/2121/2122212,=|1|2+12,sigmoid,11+221+2,=12,=21/211/21211112212,12/2111/212111112/2121/2122212,=21/211/21211112212,|1=12/2111/2121111,|2=12/2121/2122212,=|1|2+12,=12,1111,=112111+1111,=21/211/2,1211+111121111,+1221221+122212,=21/211/21211112212,=|1|2+12,=12,=11111111+1111,2212,=212221+2212,+12,EndofWarning,1|,=,1=2=,=12112111+12212+12,b,1|=+,Ingenerativemo