03 - Incremental Learning from Noisy Data

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1、MachineLearning1:317-354,1986?1986KluwerAcademicPublishers,Boston-ManufacturedinTheNetherlandsIncrementalLearningfromNoisyDataJEFFREYC.SCHLIMMERRICHARDH.GRANGER,JR.(SCHLIMMER@ICS.UCI.EDU)(GRANGER@ICS.UCI.EDU)IrvineComputationalIntelligenceProject,DepartmentofInformation

2、andComputerScience,UniversityofCalifornia,Irvine,CA92717,U.S.A.(ReceivedMarch5,1986)(RevisedMay2,1986)Keywords:learningfromexamples,contingency,systematicnoise,conceptdrift,constructiveinductionAbstract.Inductionofaconceptdescriptiongivennoisyinstancesisdifficultandisfu

3、rtherexacerbatedwhentheconceptsmaychangeovertime.Thispaperpresentsasolutionwhichhasbeenguidedbypsychologicalandmathematicalresults.Themethodisbasedonadistributedconceptdescriptionwhichiscomposedofasetofweighted,symboliccharacterizations.Twolearningprocessesincrementally

4、modifythisdescription.Oneadjuststhecharacterizationweightsandanothercreatesnewcharacteriza-tions.Thelatterprocessisdescribedintermsofasearchthroughthespaceofpossibilitiesandisshowntorequirelinearspacewithrespecttothenumberofattribute-valuepairsinthedescriptionlanguage.T

5、hemethodutilizespreviouslyacquiredconceptdefinitionsinsubsequentlearningbyaddinganattributeforeachlearnedconcepttoinstancedescriptions.AprogramcalledSTAGGERfullyembodiesthismethod,andthispaperreportsonanumberofempiricalanalysesofitsperformance.Sinceunderstandingtherelat

6、ionshipsbetweenanewlearningmethodandexistingonescanbedifficult,thispaperfirstreviewsaframeworkfordiscussingmachinelearningsystemsandthendescribesSTAGGERinthatframework.1.IntroductionTheabilitytoadapttotheenvironmentisanessentialqualityforanyintelligentmechanism.Fordomai

7、nsinwhichlearnershaveextensivepreviousknowledge,suchaselectronics,itisappropriatetoviewlearningasbeingheavilyguidedbythatpriorknowledge.However,indomainsinwhichtherearenohigh-qualitytheories,suchasweatherorfinancialprediction,somefundamentalmethodsmustbeusedtoguidelearn

8、ing.Thispaperinvestigatesabottom-uplearningtechniquewhichdoesnotrelyonastrongdomaintheory.Thespecificclassofle