简介：BasedonKKTcomplementaryconditioninoptimizationtheory,anunconstrainednon-differentialoptimizationmodelforsupportvectormachineisproposed.AnadjustableentropyfunctionmethodisgiventodealwiththeproposedoptimizationproblemandtheNewtonalgorithmisusedtofigureouttheoptimalsolution.Theproposedmethodcanfindanoptimalsolutionwitharelativelysmallparameterp,whichavoidsthenumericaloverflowinthetraditionalentropyfunctionmethods.Itisanewapproachtosolvesupportvectormachine.Thetheoreticalanalysisandexperimentalresultsillustratethefeasibilityandeffciencyoftheproposedalgorithm.

简介：Thispaperintroducesthemethodofsupportvectormachine(SVM)intothefieldofsyntheticearthquakepre-diction,whichisanon-linearandcomplexseismogenicsystem.Asanexample,weapplythismethodtopredictthelargestannualmagnitudefortheNorthChinaarea(30°E-42°E,108°N-125°N)andthecapitalregion(38°E-41.5°E,114°N-120°N)onthebasisofseismicityparametersandobservedprecursorydata.ThecorrespondingpredictionratesfortheNorthChinaareaandthecapitalregionare64.1%and75%,respectively,whichshowsthatthemethodisfeasible.

简介：Supportvectormachines(SVMs)haveshownremarkablesuccessinmanyapplications.However,thenon-smoothfeatureofobjectivefunctionisalimitationinpracticalapplicationofSVMs.Toovercomethisdisadvantage,atwicecontinuouslydifferentiablepiecewise-smoothfunctionisconstructedtosmooththeobjectivefunctionofunconstrainedsupportvectormachine(SVM),anditissuesapiecewise-smoothsupportvectormachine(PWESSVM).Comparingtotheothersmoothapproximationfunctions,thesmoothprecisionhasanobviousimprovement.ThetheoreticalanalysisshowsPWESSVMisgloballyconvergent.Numericalresultsandcomparisonsdemonstratetheclassificationperformanceofouralgorithmisbetterthanothercompetitivebaselines.

简介：纸基于复制核Hilbert空格与Multicategory支持向量机器(MSVM)分类器的错误分析有关。我们选择多项式核为纺织品商人核并且与DeLaVall茅ePoussin工具给错误估计。我们也介绍样品错误的标准评价，并且导出明确的学习率。关键词支持向量机器分类-学习的率-复制核Hilbert空格-DeLaVall茅ePoussin工具AMS(2010)题目分类42A08-68T05-62J02由中华人民共和国的国家NSF(10871226)支持了。

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简介：MicroRNAs(miRNAs)是一个家庭(2123nt)突然，规章的非编码的RNA处理了从长(70110nt)miRNA先锋(pre-miRNAs)。识别真、假的先锋在miRNAs的计算鉴定起一个重要作用。一些数字特征从先锋序列和他们的第二等的结构被提取了适合一些分类方法；然而，他们可以失去在序列和结构隐藏的一些有用地歧视的信息。在这研究，pre-miRNA序列和他们的第二等的结构直接被用来基于在二个序列之间的加权的Levenshtein距离构造一个指数的核。这个字符串内核然后为检测真、假的pre-miRNAs与支持向量机器(SVM)被相结合。在331上基于训练真、假的人的pre-miRNAs的样品，在SVM的2个关键参数被5褶层选择有不同5褶层分区的十字确认和格子搜索，和5条认识被执行。在16独立人士之中，测试从3人，8动物，2工厂，1个病毒，和2人工地假的人设定pre-miRNAs，我们的方法统计上在11个集合上超过以前的基于SVM的技术包括3人，7动物，和1假人的pre-miRNAs。特别地，有通常在以前的工作被排除的多重环的premiRNAs正确地与92.66%的精确性在这研究被识别。

简介：Supportvectormachines(SVMs)havebeenintensivelyappliedinthedomainsofspeechrecognition,textcategorization,andfaultsdetection.However,thepracticalapplicationofSVMsislimitedbythenon-smoothfeatureofobjectivefunction.Toovercomethisproblem,anovelsmoothfunctionbasedonthegeometryofcircletangentisconstructed.Itsmoothesthenon-differentiabletermofunconstrainedSVM,andalsoproposesacircletangentsmoothSVM(CTSSVM).Comparedwithothersmoothapproachingfunctions,itssmoothprecisionhadanobviousimprovement.TheoreticalanalysisprovedtheglobalconvergenceofCTSSVM.NumericalexperimentsandcomparisonsshowedCTSSVMhadbetterclassificationandlearningefficiencythancompetitivebaselines.

简介：这篇论文的目的是提供错误分析为多范畴支持向量机器(MSVM)classificaton问题。我们为MSVM建立一致集聚途径并且估计错误分类错误。我们这里克服的主要困难是围住偏移量向量。作为结果，当核多项式的度足够大时，我们证实有多项式核的MSVM分类算法总是是有效的。最后，集中和例子的率被给表明主要结果。

简介：Licenseplaterecognition(LPR)isanimageprocessingtechnologythatisusedtoidentifyvehiclesbytheirlicenseplates.ThispaperpresentsalicenseplaterecognitionalgorithmforSaudicarplatesbasedonthesupportvectormachine(SVM)algorithm.ThenewalgorithmisefficientinrecognizingthevehiclesfromtheArabicpartoftheplate.Theperformanceofthesystemhasbeeninvestigatedandanalyzed.Therecognitionaccuracyofthealgorithmisabout93.3%.

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简介：Inthispaper,wepresentanovelSupportVectorMachineactivelearningalgorithmforeffective3Dmodelretrievalusingtheconceptofrelevancefeedback.Theproposedmethodlearnsfromthemostinformativeobjectswhicharemarkedbytheuser,andthencreatesaboundaryseparatingtherelevantmodelsfromirrelevantones.Whatitneedsisonlyasmallnumberof3Dmodelslabelledbytheuser.Itcangrasptheuser'ssemanticknowledgerapidlyandaccurately.Experimentalresultsshowedthattheproposedalgorithmsignificantlyimprovestheretrievaleffectiveness.Comparedwithfourstate-of-the-artqueryrefinementschemesfor3Dmodelretrieval,itprovidessuperiorretrievalperformanceafternomorethantworoundsofrelevance

简介：Inthisstudy,wepresentaconstructivealgorithmfortrainingcooperativesupportvectormachineensembles(CSVMEs).CSVMEcombinesensemblearchitecturedesignwithcooperativetrainingforindividualSVMsinensembles.Unlikemostpreviousstudiesontrainingensembles,CSVMEputsemphasisonbothaccuracyandcollaborationamongindividualSVMsinanensemble.AgroupofSVMsselectedonthebasisofrecursiveclassifiereliminationisusedinCSVME,andthenumberoftheindividualSVMsselectedtoconstructCSVMEisdeterminedby10-foldcross-validation.ThiskindofSVMEhasbeentestedontwoovariancancerdatasetspreviouslyobtainedbyproteomicmassspectrometry.BycombiningseveralindividualSVMs,theproposedmethodachievesbetterperformancethantheSVMEofallbaseSVMs.

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简介：在这篇论文，支持向量基于机器的多模型预兆的控制被建议，在哪个SVM分类与SVM结合很好回归。起初，每工作环境被SVM回归和支持向量机器建模基于网络的模型相应于每环境的预兆的控制(SVMN-MPC)算法被开发，然后一个多班SVM模型被建立认出多重操作条件。至于控制，当前的环境被多班SVM模型识别然后相应SVMN-MPC控制器在每采样片刻被激活。当模特儿建议，切换和控制器设计在模拟结果被表明。

简介：水库的大小(M)的预言导致了地震是在地震工程的一项重要任务。在这篇文章，我们采用一台支持向量机器(SVM)和Gaussian过程回归(GPR)因为水库的预言基于水库参数导致了地震M。全面参数(E)和最大的水库深度(H)被看作输入到SVM和GPR。我们给一个方程因为水库的决心导致了地震M。发达SVM和GPR与人工的神经网络(ANN)方法相比。发达SVM和GPR是为水库的预言的有效工具的结果表演导致了地震M。

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简介：Supportvectormachines(SVM)receivedwideattentionforitsexcellentabilitytolearn,ithasbeenappliedinmanyfields.AreviewoftheapplicationofSVMinwelddefectdetectionandrecognitionofX-rayimageisbeenpresented.WewillshowsomecommonlyusedmethodsofwelddefectdetectionandrecognitionusingSVM,andtheadvantagesanddisadvantagesofeachmethodwillbediscussed.SVMappearstobepromisinginwelddefectdetectionandrecognition,butfutureresearchisneededbeforeitfullymatureinthisfiled.

简介：Afaultdiagnosismodelisproposedbasedonfuzzysupportvectormachine(FSVM)combinedwithfuzzyclustering(FC).Consideringtherelationshipbetweenthesamplepointandnon-selfclass,FCalgorithmisappliedtogeneratefuzzymemberships.Inthealgorithm,sampleweightsbasedonadistributiondensityfunctionofdatapointandgeneticalgorithm(GA)areintroducedtoenhancetheperformanceofFC.Thenamulti-classFSVMwithradialbasisfunctionkernelisestablishedaccordingtodirectedacyclicgraphalgorithm,thepenaltyfactorandkernelparameterofwhichareoptimizedbyGA.Finally,themodelisexecutedformulti-classfaultdiagnosisofrollingelementbearings.Theresultsshowthatthepresentedmodelachieveshighperformancesbothinidentifyingfaulttypesandfaultdegrees.TheperformancecomparisonsofthepresentedmodelwithSVManddistance-basedFSVMfornoisycasedemonstratethecapacityofdealingwithnoiseandgeneralization.

简介：基于小浪包转变(WPT)，基因算法(GA)，神经网络(BPNN)和支持向量用机器制造的背繁殖(SVM)，柴油机引擎阀门清理的一个差错诊断方法被介绍。与力量光谱密度分析，与运用条件的引擎有关的典型频率能从颤动信号被提取。小浪系数和根的最大的单个值(BSV)意味着在典型频率亚乐队的颤动的平方(RMS)值在颤动信号的第三水平分解的目的被提取，并且他们被用作BPNN或SVM的输入向量。为了避免，在本地最小被套住，GA被采用。正常和处于不同阀门清理条件测量的差错颤动信号被分析。BPNN，GA背繁殖神经网络(GA-BPNN)，SVM和GA-SVM被用于为不同特征的抽取训练并且测试，并且分类精确性和训练时间与相比决定最佳差错分类器和特征选择。试验性的结果证明建议特征和分类算法给100%的分类精确性。

简介：Thispaperproposesanewmethodtopredictthecoronaonsetvoltageforarodplaneairgap,basedonthesupportvectormachine(SVM).BecausetheSVMisnotlimitedbythesize,dimensionandnonlinearityofthesamples,thismethodcanrealizeaccuratepredictionwithfewtrainingdata.Onlyelectricfieldfeaturesarechosenastheinput;nogeometricparameterisincluded.Therefore,theexperimentdataofonekindofelectrodecanbeusedtopredictthecoronaonsetvoltagesofotherelectrodeswithdifferentsizes.Withtheexperimentaldataobtainedbyozonedetectiontechnology,andexperimentaldataprovidedbythereference,theefficiencyoftheproposedmethodisvalidated.Accuratepredictedresultswithanaveragerelativelessthan3%areobtainedwithonly6experimentaldata.