機(jī)器學(xué)習(xí)與模式識(shí)別的關(guān)系(英文版)

題目：機(jī)器學(xué)習(xí)與模式識(shí)別學(xué)號(hào)：02115***姓名：*** MachineLearningandPatternRecognitionAbstractRecently,machinelearninghasdevelopedrapidlyininformationfield.Also,ithasacloserelationshipwithpatternrecognition.Machininglearninghasbeenappliedtopatternrecognitionsuccessfully.Therefore,thepaperdescribesthebasiccharacteristicsofmachinelearningandpatternrecognition,whichincludestheconcepts，development，applicationandclassification.Italsoprovidesanapplicationperspectiveforunderstandingtheconceptsofmachiningandpatternrecognition.Keywords:MachineLearningPatternRecognition0.IntroductionMachinelearningisoneofthecoreproblemsofartificialintelligenceresearch.Itsapplicationhasbeenthroughoutallbranchesofartificialintelligence,suchasexpertsystems,automatedreasoninginthefieldofnaturallanguageunderstanding,patternrecognition,computervision,intelligentrobotics.Justasitsnameimplies,Machinelearningistoletthecomputertolearnsomewaytoimproveitsperformance.Patternrecognitioncanbeseenassomethingwhichcandividedifferentobjectsintodifferentcategories.Humanscandeepentheirunderstandingofthingsthroughcontinuouslearning,similarlythepatternrecognitionsystembasedonsimulatinghumanintelligencealsoneedstoimproveitsclassificationperformancethroughmachinelearningalgorithmimprovements,sothecontactbetweenmachinelearningandpatternrecognitionisgettingcloserandcloser.Thisarticlewillexplainthebasicconceptsofmachinelearningandpatternrecognition,patternrecognitionanalysisinseveralmachinelearningalgorithms.1.MachineLearning1.1ThedefinitionofmachinelearningCurrently,theaccuratedefinitionofmachinelearning:forcertainassignmentTandperformancemetricsP,ifacomputerprogramtomeasuretheperformanceofPandalongwiththeexperienceofself-improvementonT,thenwecallthecomputerprogramislearningfromexperienceE.1.2TheworkingmechanismofthemachinelearningsystemTheenvironmentprovidescertaininformationtothelearningpartsofthesystem,thenthelearningpartusesthisinformationtomodifyitsknowledgebasetoenhancetheperformanceofexecutionpart;Theexecutiondoitsworkaccordingtheknowledgebase,alsobringbacktheacquiredinformationtolearningpart.Theprocesscanbeseenasacertainprocessthatthemachinelearningsystemacquireknowledgeautomaticallywithinformationwhichareprovidedbyinternalandexternalenvironment.EnvironmentLearningpartKnowledgebaseExecutionpart1.3ThedesignofthemachinelearningsystemTherearemainlytwopartsthatneedbetakenintoconsiderationwhendesigningaperfectmachinelearningsystem:Modelselectionanddesign,Learningalgorithmselectionanddesign.Differentmodelsdeterminedifferentobjectivefunctionsanddifferentlearningmechanisms.Thecomplexityandcapacityofalgorithmdeterminethecapacityandefficiencyofthelearningsystem.Alsothesizeoftrainingsamplesandfeatureselectionproblemarethekeyfactorswhichwillconstrainmachinelearningsystemperformance.2.MachinelearningalgorithminpatternrecognitionPatternrecognitionmeansthatweshouldanalyzeperceptionsignal.Itisaprocessofidentificationandinterpretation.Wecandrawapicturetodescribethisprocess.獲取數(shù)據(jù)預(yù)處理特征生成特征選擇模式分類后處理機(jī)器學(xué)習(xí)Thecoreissueofmachinelearningissearchingproblems.Asfordifferentapplicationmodels,theresearchershavedesignedsomedifferentsearchingalgorithms.Currentlyinthefieldofpatternrecognition,weoftenusegeneticalgorithms,neuralnetworks,supportvectormachines,k-nearestneighbormethodandothermachinelearningalgorithms.2.1GeneticalgorithmCharacteristicdimensionisamajorprobleminmachinelearning,becausethecharacteristicspresentedfromcertainmodelhavedifferentweightsinreflectingthenatureofthings.Butsomeshowednosignificantcontributiontothecatagories,evenredundant,sothefeatureselectionprocessisverycritical.Geneticalgorithmcansolvethisproblemtosomeextendasaoptimizationalgorithm.Geneticalgorithmnotonlycanchoosethefeaturethatnotonlyreflectstheoriginalinformation,butalsohaveasignificantimpactontheclassificationresults.TherearethreekindsofoperationinGA.Selection-reproduction,crossover,aswellasmutation.Weusuallydoasfollows:ChooseNchromosomesfrompopulationSinNseparatetimes.TheprobabilityofoneindividualbeingchosenisP(xi).ThecomputationalformulaofP(xi):Thereisachancethatthechromosomesofthetwoparentsarecopiedunmodifiedasoffspring,orrandomlyrecombined(crossover)toformoffspring.Alsothereisachancethatageneofachildischangedrandomly.Generallythechanceofmutationislow.GAhavefourbasicelementsfromthepresent:codingstrategies;settinginitialpopulation;designoffitnessfunction;geneticoperatorsdesign,chooseoperator,crossoveroperator,mutationoperator,andthesehavebeenaimportantpointsinimproving.2.2ArtificialneuralnetworksNeuralnetworkisanewtechnologyinthefieldofmachinelearning.Manypeoplehaveheardoftheword,butfewpeoplereallyunderstandwhatitis.Thebasicneuralnetworkfunctions,includingitsgeneralstructure,relatedterms,typesandapplications.Inpatternrecognitionapplications,aclassifierusinganeuralnetworkisdesignedbyarelativelysmallnumberofneuronsconnectedtogetheraccordingtocertainrulesofnetworksystem,andeachneuroninthenetworkhavethesamestructure.Neuronstypicallyexpressedasamultiple-input,single-outputnonlinearelements,itsstructurecanbedesignedlikethis:Asalinklearningalgorithm,neuralnetworkfeaturesare:parallelprocessingofinformation,storageanddistributionofstrongfaulttolerance;self-learning,self-organizationandself-applicability.Throughtraining,theneuralnetworkcanautomaticallyadjustitsnetworkconfigurationparameterstosimulatethenonlinearrelationshipbetweeninputandoutput,sowhenwegivethenetworksomeinputs,wecangettherightclassification.2.3SupportvectormachinesThesizeoftrainingsamplesinmachinelearningsysteminfluencetheabilityofgeneralizationlearningsystem.Inmachinelearning,supportvectormachines(SVMs,alsosupportvectornetworks)aresupervisedlearningmodelswithassociatedlearningalgorithmsthatanalyzedataandrecognizepatterns,usedforclassificationandregressionanalysis.Givenasetoftrainingexamples,eachmarkedasbelongingtooneoftwocategories,anSVMtrainingalgorithmbuildsamodelthatassignsnewexamplesintoonecategoryortheother,makingitanon-probabilisticbinarylinearclassifier.AnSVMmodelisarepresentationoftheexamplesaspointsinspace,mappedsothattheexamplesoftheseparatecategoriesaredividedbyacleargapthatisaswideaspossible.Newexamplesarethenmappedintothatsamespaceandpredictedtobelongtoacategorybasedonwhichsideofthegaptheyfallon.Inadditiontoperforminglinearclassification,SVMscanefficientlyperforma