简介：Inthispaper,thenormalapproximationrateandtherandomweightingapproximationrateoferrordistributionofthekernelestimatorofconditionaldensityfunctionf(y!|x)arestudied.Theresultsmaybeusedtoconstructtheconfidenceintervaloff(y|x).

简介：Theauthorsderivelawsoftheiteratedlogarithmforkernelestimatorofregressionfunctionbasedondirectionaldata.Theresultsaredistributionfreeinthesensethattheyaretrueforalldistributionsofdesignvariable.

简介：一个基于核的判别式分析方法把核称为直接判别式分析被采用，它把直接线性的判别式分析的优点与核诡计的相结合。为了表明它的更好的坚韧性到真实的脸的复杂、非线性的变化，想象，例如照明，面部表情，规模和姿势变化，实验在Olivetti研究实验室，耶鲁和自我造的脸上被执行数据库。Theresults与核主管部件分析和核相对照显示那线性判别式分析，方法能完成更低(7%)用仅仅特征的一个很小的集合的错误率。而且，一个新改正的内核模型被建议改进识别性能。试验性的结果证实它的优势(1%以识别评价)到另外的多项式，核当模特儿。

简介：LetGbeap-seriesgroupandΩbeacompactsubgroupofG.Letλ(x,r)andλ,(x,r)beA-belp-poissontypekernelandproducttypekernelOnΩrespectively.Inthispaperwediscusstheap-proximationpropertiesofsuchkernels,givetheestimate5oftheirmoments,obtainthedirectandin-verseapproximationtheorems.

简介：TheNeighborhoodPreservingEmbedding(NPE)algorithmisrecentlyproposedasanewdimensionalityreductionmethod.However,itisconfinedtolineartransformsinthedataspace.Forthis,basedontheNPEalgorithm,anewnonlineardimensionalityreductionmethodisproposed,whichcanpreservethelocalstructuresofthedatainthefeaturespace.First,combinedwiththeMercerkernel,thesolutiontotheweightmatrixinthefeaturespaceisgottenandthenthecorrespondingeigenvalueproblemoftheKernelNPE(KNPE)methodisdeduced.Finally,theKNPEalgorithmisresolvedthroughatransformedoptimizationproblemandQRdecomposition.Theexperimentalresultsonthreereal-worlddatasetsshowthatthenewmethodisbetterthanNPE,KernelPCA(KPCA)andKernelLDA(KLDA)inperformance.

简介：ＣＯＮＳＩＳＴＥＮＣＹＦＯＲＫＥＲＮＥＬＥＳＴＩＭＡＴＥＯＦＣＯＮＤＩＴＩＯＮＡＬＦＵＮＣＴＩＯＮ￥ＨＵＳｈｕｈｅ（ＤｅｐａｒｔｍｅｎｔＯｆＭａｔｈｅｍｏｔｉｃｓ，ＡｎｈｕｉＵｎｉｖｅｒｓｉｔｙ，Ｈｅｆｅｉ２３００３９，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：...

简介：Integralcollisionkerneliselucidatedusingexperimentalresultsfortitania,silicaandaluminananoparticlessynthesizedbyFCVDprocess,andtitaniasubmicronparticlessynthesizedinatubefurnacereactor.Theintegralcollisionkernelwasobtainedfromaparticlenumberbalanceequationbytheintegrationofcollisionratesfromthekinetictheoryofdilutegasesforthefree-moleculeregime,fromtheSmoluchowskitheoryforthecontinuumregime,andbyasemi-empiricalinterpolationforthetransitionregimebetweenthetwolimitingregimes.Comparisonshavebeenmadeonparticlesizeandtheintegralcollisionkernel,showingthatthepredictedintegralcollisionkernelagreedwellwiththeexperimentalresultsinKnudsennumberrangefromabout1.5to20.

简介：Inthispaper,theauthorgivestheweightedweakLipschitzboundednesswithpowerweightforroughmultilinearintegraloperators.Asimplewayisobtainedthatiscloselylinkedwithaclassofroughfractionalintegraloperators.

简介：关联向量机器(RVM)的普通信号分类的一条新途径基于向量机器(SVM)和RVM被比较并且分析的支持的核方法被介绍，二发信号分类器。信号的开始的几个柔韧的特征作为分类器的输入被提取，然后，内核思维被用来隐含地印射特征向量到高维的特征空格，和多班RVM和SVM分类器被设计完成AM，CW，SSB，MFSK和MPSK信号识别。模拟结果看了那什么时候选了有的合适的参数，RVM和SVM为可比较的精确性，但是RVM有更少的学习时间和基础功能。RVM的分类速度比SVM快得多。

简介：Receiveroperatingcharacteristic(ROC)curvesareoftenusedtostudythetwosampleprobleminmedicalstudies.However,mostdatainmedicalstudiesarecensored.UsuallyanaturalestimatorisbasedontheKaplan-Meierestimator.InthispaperweproposeasmoothedestimatorbasedonkerneltechniquesfortheROCcurvewithcensoreddata.Thelargesamplepropertiesofthesmoothedestimatorareestablished.Moreover,deficiencyisconsideredinordertocomparetheproposedsmoothedestimatoroftheROCcurvewiththeempiricalonebasedonKaplan-Meierestimator.ItisshownthatthesmoothedestimatoroutperformsthedirectempiricalestimatorbasedontheKaplan-Meierestimatorunderthecriterionofdeficiency.Asimulationstudyisalsoconductedandarealdataisanalyzed.

简介：TheFFDalgorithmisoneofthemostfamousalgorithmsfortheclassicalbinpackingproblem.Inthispaper,someversionsoftheFFDalgorithmareconsideredinseveralbinpackingproblems.Especially,twoofthemappliedtothebinpackingproblemwithkernelitemsareanalyzed.Tightworst-caseperformanceratiosareobtained.

简介：Hyperspectralimageprovidesabundantspectralinformationforremotediscriminationofsubtledifferencesingroundcovers.However,theincreasingspectraldimensions,aswellastheinformationredundancy,maketheanalysisandinterpretationofhyperspectralimagesachallenge.Featureextractionisaveryimportantstepforhyperspectralimageprocessing.Featureextractionmethodsaimatreducingthedimensionofdata,whilepreservingasmuchinformationaspossible.Particularly,nonlinearfeatureextractionmethods(e.g.kernelminimumnoisefraction(KMNF)transformation)havebeenreportedtobenefitmanyapplicationsofhyperspectralremotesensing,duetotheirgoodpreservationofhigh-orderstructuresoftheoriginaldata.However,conventionalKMNForitsextensionshavesomelimitationsonnoisefractionestimationduringthefeatureextraction,andthisleadstopoorperformancesforpost-applications.Thispaperproposesanovelnonlinearfeatureextractionmethodforhyperspectralimages.Insteadofestimatingnoisefractionbythenearestneighborhoodinformation(withinaslidingwindow),theproposedmethodexplorestheuseofimagesegmentation.Theapproachbenefitsbothnoisefractionestimationandinformationpreservation,andenablesasignificantimprovementforclassification.Experimentalresultsontworealhyperspectralimagesdemonstratetheefficiencyoftheproposedmethod.ComparedtoconventionalKMNF,theimprovementsofthemethodontwohyperspectralimageclassificationare8and11％.Thisnonlinearfeatureextractionmethodcanbealsoappliedtootherdisciplineswherehigh-dimensionaldataanalysisisrequired.

简介：§1.IntroductionandMainResultLet(X,F)beaJBrXR'-valuedvector.AssumethatwhenX=xisgiven,thereexistsaconditionaldensityofYtobedenotedbyf(y[x),whichisaBorel-measurablefunctionof(x,y).Notethatwedonotassumetheexistenceofadensityfunctionof(X,F).Let(X-i,fi),—,(Xn,Fn)bei.i.d.samplesof(X,F).Ourpurposeistoestimatef(y\x)basedonthesesamples.Thisisaninterestingprobleminviewofeitherpuretheoryorpracticalapplications.MotivatedbytheideasuggestedinkernelandNNestimationsinthetheoryofnonparametricregressionanddensityestimates,thefirstauthorproposesthefollowingtwoclassesofestimatorsoff(y\x):

简介：Inthispaper,theauthorsobtainsharpupperandlowerboundsfortheheatkernelassociatedwithJacobitransform,andgetsomeanaloguesofHardy'sTheoremforJacobitransformbyusingthesharpestimateoftheheatkernel.

简介：Amajordifficultyinmultivariablecontroldesignisthecross-couplingbetweeninputsandoutputswhichobscurestheeffectsofaspecificcontrollerontheoverallbehaviorofthesystem.Thispaperconsiderstheapplicationofkernelmethodindecouplingmultivariableoutputfeedbackcontrollers.Simulationresultsarepresentedtoshowthefeasibilityftheproposedtechnique.

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简介：Thekernelbasedtrackinghastwodisadvantages:thetrackingwindowsizecannotbeadjustedefficiently,andthekernelbasedcolordistributionmaynothaveenoughabilitytodiscriminateobjectfromclutterbackground.Forboostingupthefeature'sdiscriminatingability,bothscaleinvariantfeaturesandkernelbasedcolordistributionfeaturesareusedasdescriptorsoftrackedobject.Theproposedalgorithmcankeeptrackingobjectofvaryingscalesevenwhenthesurroundingbackgroundissimilartotheobject'sappearance.

简介：Ｅｐｉｃｅｎｔｒａｌ　ｄｉｓｔｒｉｂｕｔｉｏｎ　ｉｎ　１９９４Ｐｅｉ－ＳｈａｎＣＨＥＮ（陈培善）（ＩｎｓｔｉｔｕｔｅｏｆＧｅｏｐｈｙｓｉｃｓ，ＳｔａｔｅＳｅｉｓｍｏｌｏｇｉｃａｌＢｕｒｅａｕ，Ｂｅｉｊｉｎｇ１０００８１，Ｃｈｉｎａ）Ｐｅｉ－ＳｈａｎＣ...

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