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1、Chapter13Presentingsomemachine-learningmethodsMachinelearningisaboutprogrammingcomputerstolearnandtoimproveautomaticallywithexperience.Whilecomputerscannotyetlearnaswellaspeople,algorithmshavebeendevisedthatareeffectiveforcertaintypesoflearningtasks,andatheoreticalunderstandingoflearnin
2、ghasemerged.Computerprogramsdeveloped,exhibitingusefultypesoflearning,especiallyinspeechrecognitionanddatamining(seeMitchell[1997]).Machinelearningisinherentlyamultidisciplinaryfielddrawingresultsfromartificialintelligence,probabilityandstatistics,computationalcomplexitytheory,controltheo
3、ry,informationtheory,philosophy,psychology,neurobiology,andotherfields.Wearegoingtointroducesomeofthesefieldsastheyhavebeenextensivelyusedinthefinancialindustrytodevisesystematictradingstrategies.13.1Somefactsonmachine-learning13.1.1IntroductiontodataminingTherapidgrowthandintegrationofdat
4、abasesprovidedscientists,engineers,andbusinesspeoplewithavastnewresourcethatcanbeanalysedtomakescientificdiscoveries,optimiseindustrialsystems,anduncoverfinanciallyvaluablepatterns.Newmethodstargetedatlargedataminingproblemshavebeendeveloped.DataMining(DM)istheanalysisof(large)observation
5、aldatasetstofindunsuspectedrelationshipsandtosummarisethedatainnovelwaysthatarebothunderstandableandusefultothedataowner(seeHandetal.[2001]).ThereexistsseveralfunctionalitiestoDM,suchthatdatacharacterisationsummarisingthegeneralcharacteristicsorfeaturesofatargetclassofdata,anddatadiscrim
6、inationcomparingthegeneralfeaturesoftargetclassdataobjectswiththegeneralfeaturesofobjectsfromasetofcontrastingclasses.Further,associationanalysisisthediscoveryofassociationrulesshowingattributevalueconditionsoccurringfrequentlytogetherinagivensetofdata.Classificationistheprocessoffindinga
7、setofmodels(orfunctions)thatdescribeanddistinguishdataclassesorconcepts,forthepurposeofbeingabletousethemodeltopredicttheclassofobjectswhoseclasslabelisunknown.Machinelearninghavebeentraditionallyusedforclassification,wheremodelssuchasdecisiontrees,SVM,neuralnetworks,Bayesianbel