基于实例的学习ppt课件.ppt

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1、第8章基于实例的学习InstanceBasedLearning1InstanceBasedLearningk-NearestNeighborLocallyweightedregressionRadialbasisfunctionsCase-basedreasoningLazyandeagerlearning2NearestneighborKeyidea:juststorealltrainingexamplesNearestneighbor:Givenqueryinstancexq,firstlocatenearesttrainingexamplexn,thenestimate3k-Ne

2、arestneighbork-Nearestneighbor:Givenxq,Ifdiscrete-valuedtargetfunction:takevoteamongitsknearestnbrsIfreal-valuedtargetfunction:takemeanoffvaluesofk-nearestnbrs4WhenToConsiderNearestNeighborInstancesmaptopointsinLessthan20attributesperinstanceLotsoftrainingdata5Advantages&DisadvantagesAdvantagesT

3、rainingisveryfastLearncomplextargetfunctionsDon’tloseinformationDisadvantagesSlowatquerytimeEasilyfooledbyirrelevantattributes6VoronoiDiagram7BehaviorintheLimitConsiderp(x)definesprobabilitythatinstancexwillbelabeled1(positive)versus0(negative)NearestneighborAsnumberoftrainingexamples∞approache

4、sGibbsAlgorithmGibbs:withprobabilityp(x)predict1else08BehaviorintheLimitConsiderp(x)definesprobabilitythatinstancexwillbelabeled1(positive)versus0(negative)k-NearestneighborAsnumberoftrainingexamples∞andkgetslarge,approachesBayesoptimalBayesoptimal:ifp(x)>0.5thenpredict1,else0NoteGibbshasatmost

5、twicetheexpectederrorofBayesoptimal9Distance-WeightedkNNNearerneighborsaremoreheavilyweighted.Discrete-valuedReal-valuedwhereItmakessensetousealltrainingexamplesinsteadofjustk.[Shepard,1968]10CurseofDimensionalityImagineinstancesdescribedby20attributesbutonly2arerelevanttotargetfunction.Curseofd

6、imensionality:nearestnbriseasilymisleadwhenhigh-dimensionalXApproach:Stretchsomeaxesbyweight,andthencross-validation[MooreandLee,1944]11LocallyWeightedRegressionNotekNNformslocalapproximationtofforeachquerypointxq.Whynotformanexplicitapproximationforregionsurroundingxq?Fitlinearfunctiontokneares

7、tneighborsFitquadratic…Producespiecewiseapproximationtof12SeveralchoicesoferrortominimizeSquarederroroverknearestnbrsDistance-weightedsquarederroroverallnbrs…13RadialBasisFunctionNetworksGlobalapproximationtotargetfunctionin