Morethanadecadeago, combiningmultipleclassi?erswasproposedasap- siblesolutiontotheproblemsposedbythetraditionalpatternclassi?cation approachwhichinvolvedselectingthebestclassi?erfromasetofcandidates basedontheirexperimentalevaluation. Asnoclassi?erisknowntobethebest forallcasesandtheselectionofthebestclassi?erforagivenpracticaltaskis verydi?cult, diverseresearchcommunities, includingMachineLearning, N- ralNetworks, PatternRecognition, andStatistics, addressedtheengineering ...
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Morethanadecadeago, combiningmultipleclassi?erswasproposedasap- siblesolutiontotheproblemsposedbythetraditionalpatternclassi?cation approachwhichinvolvedselectingthebestclassi?erfromasetofcandidates basedontheirexperimentalevaluation. Asnoclassi?erisknowntobethebest forallcasesandtheselectionofthebestclassi?erforagivenpracticaltaskis verydi?cult, diverseresearchcommunities, includingMachineLearning, N- ralNetworks, PatternRecognition, andStatistics, addressedtheengineering problemofhowtoexploitthestrengthswhileavoidingtheweaknessesofd- ferentdesigns. Thisambitiousresearchtrendwasalsomotivatedbyempirical observationsaboutthecomplementarityofdi?erentclassi?erdesigns, natural requirementsofinformationfusionapplications, andintrinsicdi?cultiesasso- atedwiththeoptimalchoiceofsomeclassi?erdesignparameters, suchasthe architectureandtheinitialweightsforaneuralnetwork. Afteryearsofresearch, thecombinationofmultipleclassi?ershasbecomeawellestablishedandexciting researcharea, whichprovidese?ectivesolutionstodi?cultpatternrecognition problems. Aconsiderablebodyofempiricalevidencesupportsthemeritof- signingcombinedsystemswhoseaccuracyishigherthanthatofeachindividual classi?er, andvariousmethodsforthegenerationandthecombinationofm- tipleclassi?ershavebecomeavailable. However, despitetheprovedutilityof multipleclassi?ersystems, nogeneralanswertotheoriginalquestionaboutthe possibilityofexploitingthestrengthswhileavoidingtheweaknessesofdi?erent classi?erdesignshasyetemerged. Otherfundamentalissuesarealsoamatterof on-goingresearchindi?erentresearchcommunities. Theresultsachievedd- ingthepastyearsarealsospreadoverdi?erentresearchcommunities, andthis makesitdi?culttoexchangesuchresultsandpromotetheircross-fertilization. Theacknowledgmentofthefundamentalrolethatthecreationofacommon internationalforumforresearchersofthediversecommunitiescouldplayfor theadvancementofthisresearch?eldmotivatedthepresentseriesofwo- shopsonmultipleclassi?ersystems. Followingitspredecessors, MultipleCl- si?erSystems2000(SpringerISBN3-540-67704-6)and2001(SpringerISBN 3-540-42284-6), thisvolumecontainstheproceedingsoftheThirdInternational WorkshoponMultipleClassi?erSystems(MCS2002), heldattheGrandHotel ChiaLaguna, Cagliari, Italy, onJune24-26,2002. The29papersselectedby thescienti?ccommitteehavebeenorganizedinsessionsdealingwithbagging andboosting, ensemblelearningandneuralnetworks, combinationstrategies, designmethodologies, analysisandperformanceevaluation, andapplications. Theworkshopprogramandthisvolumeareenrichedwiththreeinvitedtalks givenbyJoydeepGhosh(UniversityofTexas, USA), TrevorHastie(Stanford University, USA), andSarunasRaudys(VilniusGediminasTechnicalUniversity, Lithuania). Papersweresubmittedfromresearchersofthefourdiversecom- nities, socon?rmingthatthisseriesofworkshopscanbecomeacommonforum VI Foreword forexchangingviewsandreportinglatestresearchresults. Asfortheprevious editions, thesigni?cantnumberofpapersdealingwithrealpatternrecognition applicationsareproofofthepracticalutilityofmultipleclassi?ersystems. This workshopwassupportedbytheUniversityofCagliari, Italy, theUniversityof Surrey, Guildford, UnitedKingdom, andtheDepartmentofElectricalandEl- tronicEngineeringoftheUniversityofCagliari. Allthesesupportsaregratefully acknowledged. WealsothanktheInternationalAssociationforPatternRecog- tionanditsTechnicalCommitteeTC1onStatisticalPatternRecognitionTe- niquesforsponsoringMCS2002. Wewishtoexpressourappreciationtoallthose whohelpedtoorganizeMCS2002. Firstofall, wewouldliketothankallthe membersoftheScienti?cCommitteewhoseprofess
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