论文.docx
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论文.docx
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论文
1DefiningQuestions
Ascientificfieldisbestdefinedbythecentralquestionitstudies.ThefieldofMachineLearningseekstoanswerthequestion
“Howcanwebuildcomputersystemsthatautomaticallyimprovewithexperience,andwhatarethefundamentallawsthatgovernalllearningprocesses?
”
Thisquestioncoversabroadrangeoflearningtasks,suchashowtodesignautonomousmobilerobotsthatlearntonavigatefromtheirownexperience,howtodataminehistoricalmedicalrecordstolearnwhichfuturepatientswillrespondbesttowhichtreatments,andhowtobuildsearchenginesthatautomaticallycustomizetotheiruser’sinterests.Tobemoreprecise,wesaythatamachinelearnswithrespecttoaparticulartaskT,performancemetricP,andtypeofexperienceE,ifthesystemreliablyimprovesitsperformancePattaskT,followingexperienceE.
DependingonhowwespecifyT,P,andE,thelearningtaskmightalsobecalledbynamessuchasdatamining,autonomousdiscovery,databaseupdating,programmingbyexample,etc.
MachineLearningisanaturaloutgrowthoftheintersectionofComputerScienceandStatistics.WemightsaythedefiningquestionofComputerScienceis“Howcanwebuildmachinesthatsolveproblems,andwhichproblemsareinherentlytractable/intractable?
”ThequestionthatlargelydefinesStatisticsis“Whatcanbeinferredfromdataplusasetofmodelingassumptions,withwhatreliability?
”ThedefiningquestionforMachineLearningbuildsonboth,butitisadistinctquestion.WhereasComputerSciencehasfocusedprimarilyonhowtomanuallyprogramcomputers,MachineLearningfocusesonthequestionof
howtogetcomputerstoprogramthemselves(fromexperienceplussomeinitialstructure).WhereasStatisticshasfocusedprimarilyonwhatconclusionscanbeinferredfromdata,MachineLearningincorporatesadditionalquestionsaboutwhatcomputationalarchitecturesandalgorithmscanbeusedtomosteffectivelycapture,store,index,retrieveandmergethesedata,howmultiplelearningsubtaskscanbeorchestratedinalargersystem,andquestionsofcomputationaltractability.
AthirdfieldwhosedefiningquestioniscloselyrelatedtoMachineLearningisthestudyofhumanandanimallearninginPsychology,Neuroscience,andrelatedfields.Thequestionsofhowcomputerscanlearnandhowanimalslearnmostprobablyhavehighlyintertwinedanswers.Todate,however,theinsightsMachineLearninghasgainedfromstudiesofHumanLearningaremuchweakerthanthoseithasgainedfromStatisticsandComputerScience,dueprimarilytotheweakstateofourunderstandingofHumanLearning.Nevertheless,thesynergybetweenstudiesofmachineandhumanlearningisgrowing,withmachinelearningalgorithmssuchastemporaldifferencelearningnowbeingsuggestedasexplanationsforneuralsignalsobservedinlearninganimals.OverthecomingyearsitisreasonabletoexpectthesynergybetweenstudiesofHumanLearningandMachineLearningtogrowsubstantially,astheyarecloseneighborsinthelandscapeofcorescientificquestions.
Otherfields,frombiologytoecomonicstocontroltheoryalsohaveacoreinterestinthequestionofhowsystemscanautomaticallyadaptoroptimizetotheirenvironment,andmachinelearningwilllikelyhaveanincreasingexchangeofideaswiththesefieldsoverthecomingyears.Forexample,economicsisinterestedinquestionssuchashowdistributedcollectionsofself-interestedindividualsmayformasystem(market)thatlearnspricesleadingtopareto-optimalallocationsforthegreatestcommongood.Andcontroltheory,especiallyadaptivecontroltheory,isinterestedinquestionssuchashowaservo-controlsystemcanimproveitscontrolstrategythroughexperience.Interestingly,themathematicalmodelsforadaptationintheseotherfieldsaresomewhatdifferentfromthosecommonlyusedinmachinelearning,suggestingsignificantpotentialforcross-fertilizationofmodelsandtheories.
ThefollowingsectionsdiscussthestateoftheartofMachineLearning,asampleofsuccessfulapplica-tions,andasampleofopenresearchquestions.
2StateofMachineLearning
Herewedescribesomeoftheprogressinmachinelearning,aswellasopenresearchquestions.
2.1ApplicationSuccesses
OnemeasureofprogressinMachineLearningisitssignificantreal-worldapplications,suchasthoselistedbelow.Althoughwenowtakemanyoftheseapplicationsforgranted,itisworthnotingthataslateas1985therewerealmostnocommercialapplicationsofmachinelearning.
*Speechrecognition.Currentlyavailablecommercialsystemsforspeechrecognitionallusemachinelearninginonefashionoranothertotrainthesystemtorecognizespeech.Thereasonissimple:
thespeechrecognitionaccuracyisgreaterifonetrainsthesystem,thanifoneattemptstoprogramitbyhand.Infact,manycommercialspee
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