JAM: Java agents for Meta-Learning over Distributed Databases
JAM: Java agents for Meta-Learning over Distributed Databases
JAM: Java agents for Meta-Learning over Distributed Databases
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thesameserialcodeatmultiplesiteswithoutthetime-consumingprocessofwritingparallel programsandsecond,thelearningprocessusessmallsubsetsofdatathatcantinmain implementedasadistinctserialprogram)onanumberofdatasubsets(adatareduction thecollectiveresultsthroughmeta-learning.Thisapproachhastwoadvantages,rstituses technique)inparallel(eg.<strong>over</strong>anetworkofseparateprocessingsites)andthentocombine memory.Theaccuracyofthelearnedconceptsbytheseparatelearningprocessmightbe Ourapproachtoimproveeciencyistoexecuteanumberoflearningprocesses(each<br />
lowerthanthatoftheserialversionappliedtotheentiredatasetsinceaconsiderableamount ofin<strong>for</strong>mationmaynotbeaccessibletoeachoftheindependentandseparatelearningpro- achieveaccuracylevels,comparabletothatreachedbythea<strong>for</strong>ementionedserialversion cesses.Ontheotherhand,combiningthesehigherlevelconceptsviameta-learning,may appliedtotheentiredataset.Furthermore,thisapproachmayuseavarietyofdierent learningalgorithmsondierentcomputingplat<strong>for</strong>ms.Becauseoftheproliferationofnetworksofworkstationsandthegrowingnumberofnewlearningalgorithms,ourapproachdoesnotrelyonanyspecicparallelordistributedarchitecture,noronanyparticularalgolicationshavereportedper<strong>for</strong>manceresultsonstandardtestproblemsanddatasetswithbitration,combining[3]andhierarchicaltree-structuredmeta-learningsystems.Otherpubrithm,andthusdistributedmeta-learningmayaccommodatenewsystemsandalgorithmscombining[10]tonameafew.Weshallnotrepeatthisexpositioninthispaper.Herewede- relativelyeasily.Ourmeta-learningapproachisintendedtobescalableaswellasportable<br />
scribethe<strong>JAM</strong>systemarchitecturedesignedtosupporttheseandperhapsotherapproaches discussionsofrelatedtechniques,Wolpert'sstacking[9],Breiman'sbagging[1]andZhang's andextensible.<br />
todistributeddatamining. Inpriorpublicationsweintroducedanumberofmeta-learningtechniquesincludingar-<br />
togetherthroughanetworkofDatasites.Each<strong>JAM</strong>Datasiteconsistsof: supportsthelaunchingoflearningandmeta-learning<strong>agents</strong>todistributeddatabasesites. <strong>JAM</strong>isimplementedasacollectionofdistributedlearningandclassicationprogramslinked designedasanextensionofOSenvironments.Itisadistributedmeta-learningsystemthat 3The<strong>JAM</strong>architecture <strong>JAM</strong>isarchitecturedanagentbasedsystem,adistributedcomputingconstructthatis Alocaldatabase, Alearningagent,amachinelearningprogramthatmaymigratetoothersitesasa JAVAapplet,orbelocallystoredasanativeapplicationcallablebyaJAVAapplet, Ameta-learningagent, Alocalusercongurationle, GraphicalUserInterfaceandAnimationfacilities. 2
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