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.

简介：

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

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简介：Previously,anovelclassifiercalledKernel-basedNonlinearDiscriminator(KND)wasproposedtodiscriminateapatternclassfromotherclassesbyminimizingmeaneffectofthelatter.Toconsidertheeffectofthetargetclass,thispaperintroducesanobliqueprojectionalgorithmtodeterminethecoefficientsofaKNDsothatitisextendedtoanewversioncalledextendedKND(eKND).IneKNDconstruction,thedesiredoutputvectorofthetargetclassisobliquelyprojectedontotherelevantsubspacealongthesubspacerelatedtootherclasses.Inaddition,asimpletechniqueisproposedtocalculatetheassociatedobliqueprojectionoperator.ExperimentalresultsonhandwrittendigitrecognitionshowthatthealgorithmperformesbetterthanaKNDclassifierandsomeothercommonlyusedclassifiers.

简介：象LTTng一样的踪迹工具作为与传统的调试器相比在跟踪软件上有很低的影响。为长跑，在抑制的资源和高产量环境，然而交换节点和生产服务器，例如嵌入的网络目标软件上的集体跟踪影响更加加起来。就以实行时间而且以要存储的数据的巨大的数量，开销没脱机被处理并且分析。这份报纸论述由介绍处理如此的巨大的踪迹数据产生的一个新奇方法一即时(JIT)过滤器基于跟踪系统为通过高频率事件的洪水的sieving，并且记录仅仅相关的那些，当一个特定的条件被满足时。与微小的过滤费用，用户能滤出仅仅兴趣的事件上的大多数事件和焦点。我们证明在某些情形，编的过滤器证明是三的JIT预定比类似的解释过滤器有效的更多。我们也证明与过滤器谓语和上下文变量的增加的数字，有一些JIT的JIT编译增加的好处编了比他们的解释对应物快甚至三倍的过滤器。我们进一步介绍新体系结构，用我们的过滤系统，它能启用在跟踪高效地分享数据的VM(虚拟机)的核和过程之间的合作跟踪。我们通过用户能动态地通过跟踪其效果在跟踪跟踪VM的核作决定的被反映的VM的userspace指定syscall潜伏的一种跟踪情形表明它的使用。我们在我们的分享的记忆系统上比较数据存取表演并且在为合作跟踪分享的传统的数据上显示出几乎100次改进。

简介：Thispaperpresentsasimplenonparametricregressionapproachtodata-drivencomputinginelasticity.Weapplythekernelregressiontothematerialdataset,andformulateasystemofnonlinearequationssolvedtoobtainastaticequilibriumstateofanelasticstructure.Preliminarynumericalexperimentsillustratethat,comparedwithexistingmethods,theproposedmethodfindsareasonablesolutionevenifdatapointsdistributecoarselyinagivenmaterialdataset.

简介：Withthevigorousexpansionofnonlinearadaptivefilteringwithreal-valuedkernelfunctions,itscounterpartcomplexkerneladaptivefilteringalgorithmswerealsosequentiallyproposedtosolvethecomplex-valuednonlinearproblemsarisinginalmostallreal-worldapplications.ThispaperfirstlypresentstwoschemesofthecomplexGaussiankernel-basedadaptivefilteringalgorithmstoillustratetheirrespectivecharacteristics.ThenthetheoreticalconvergencebehaviorofthecomplexGaussiankernelleastmeansquare(LMS)algorithmisstudiedbyusingthefixeddictionarystrategy.ThesimulationresultsdemonstratethatthetheoreticalcurvespredictedbythederivedanalyticalmodelsconsistentlycoincidewiththeMonteCarlosimulationresultsinbothtransientandsteady-statestagesfortwointroducedcomplexGaussiankernelLMSalgonthmsusingnon-circularcomplexdata.Theanalyticalmodelsareabletoberegardasatheoreticaltoolevaluatingabilityandallowtocomparewithmeansquareerror(MSE)performanceamongofcomplexkernelLMS(KLMS)methodsaccordingtothespecifiedkernelbandwidthandthelengthofdictionary.