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1、AFewUsefulThingstoKnowaboutMachineLearningPedroDomingosDepartmentofComputerScienceandEngineeringUniversityofWashingtonSeattle,WA98195-2350,U.S.A.pedrod@cs.washington.eduABSTRACTcorrectoutputytforfutureexamplesxt(e.g.,whethertheMachinelearningalgorithmscanfigureouthow
2、toperformspamfiltercorrectlyclassifiespreviouslyunseenemailsasimportanttasksbygeneralizingfromexamples.Thisisof-spamornotspam).tenfeasibleandcost-effectivewheremanualprogrammingisnot.Asmoredatabecomesavailable,moreambitious2.LEARNING=REPRESENTATION+problemscanbetackled
3、.Asaresult,machinelearningisEVALUATION+OPTIMIZATIONwidelyusedincomputerscienceandotherfields.However,Supposeyouhaveanapplicationthatyouthinkmachinedevelopingsuccessfulmachinelearningapplicationsrequireslearningmightbegoodfor.Thefirstproblemfacingyouasubstantialamounto
4、f“blackart”thatishardtofindinisthebewilderingvarietyoflearningalgorithmsavailable.textbooks.ThisarticlesummarizestwelvekeylessonsthatWhichonetouse?Thereareliterallythousandsavailable,machinelearningresearchersandpractitionershavelearned.andhundredsmorearepublishedeac
5、hyear.ThekeytonotTheseincludepitfallstoavoid,importantissuestofocuson,gettinglostinthishugespaceistorealizethatitconsistsandanswerstocommonquestions.ofcombinationsofjustthreecomponents.Thecomponentsare:1.INTRODUCTIONMachinelearningsystemsautomaticallylearnprogramsfr
6、omRepresentation.Aclassifiermustberepresentedinsomedata.Thisisoftenaveryattractivealternativetomanuallyformallanguagethatthecomputercanhandle.Con-constructingthem,andinthelastdecadetheuseofmachineversely,choosingarepresentationforalearneristan-learninghasspreadrapidl
7、ythroughoutcomputersciencetamounttochoosingthesetofclassifiersthatitcanandbeyond.MachinelearningisusedinWebsearch,spampossiblylearn.Thissetiscalledthehypothesisspacefilters,recommendersystems,adplacement,creditscoring,ofthelearner.Ifaclassifierisnotinthehypothesisfraud
8、detection,stocktrading,drugdesign,andmanyotherspace,itcannotbelearned.Arelatedquestion,whichapplications.ArecentreportfromtheMcKinseyGloba
