简介：Inthispaper,theactivelearningmechanismisproposedtobeusedinclassifiersystemstocopewithcomplexproblems:anintelligentagentleavesitsownsignalsintheenvironmentandlatercollectsandemploysthemtoassistitslearningprocess.Principlesandcomponentsofthemechanismareoutlined,followedbytheintroductionofitspreliminaryimplementationinanactualsystem.Anexperimentwittesysteminadynamicproblemisthenintroduced,togetherwithdiscussionsoveritsresults.Thepaperisconcludedbypointingoutsomepossibleimprovementsthatcanbemadetotheproposedframework.

简介：Thispaperproposesasupervisedtraining-testmethodwithGeneticProgramming(GP)forpatternclassification.Comparedandcontrastedwithtraditionalmethodswithregardtodeterministicpatternclassifiers,thismethodistrueforbothlinearseparableproblemsandlinearnon-separableproblems.Forspecifictrainingsamples,itcanformulatetheexpressionofdiscriminatefunctionwellwithoutanypriorknowledge.Atlast,anexperimentisconducted,andtheresultrevealsthatthissystemiseffectiveandpractical.

简介：Researchontheflowfieldinsideaturboclassifieriscomplicatedthoughimportant.Accordingtothestochastictrajectorymodelofparticlesingas-solidtwo-phaseflow,andadoptingthePHOENICScode,numericalsimulationiscarriedoutontheflowfield,includingparticletrajectory,intheinnercavityofaturboclassifier,usingbothstraightandbackwardcrookedelbowblades.Computationresultsshowthatwhenthebackwardcrookedelbowbladesareused,themixedstreamthatpassesthroughthetwobladesproducesavortexinthepositivedirectionwhichcounteractstheattachedvortexintheoppositedirectionduetothehigh-speedturborotation,makingtheflowsteadier,thusimprovingboththegradeefficiencyandprecisionoftheturboclassifier.Thisresearchprovidespositivetheoreticalevidencesfordesigningsub-micronparticleclassifierswithhighefficiencyandaccuracy.

简介：情绪分类上的State-of-the-arts研究典型地是域依赖者并且限制域。在这份报纸，我们试图减少领域相关性并且由建议一个有效多域情绪分类算法同时改进全面性能。我们的方法采用多重分类器联合的途径。在这条途径，我们首先与领域独立训练单个领域分类器特定的数据，然后为最后的决定联合分类器。我们的实验比两个挑选领域分类途径的证明这条途径更好表现(个别地使用训练数据)并且混合领域分类途径(都简单地联合训练数据)。特别地，有加权的和统治的分类器联合在单个领域分类上获得27.6%的平均错误减小。

简介：WhenusingAdaBoosttoselectdiscriminantfeaturesfromsomefeaturespace(e.g.Gaborfeaturespace)forfacerecognition,cascadestructureisusuallyadoptedtoleveragetheasymmetryinthedistributionofpositiveandnegativesamples.EachnodeinthecascadestructureisaclassifiertrainedbyAdaBoostwithanasymmetriclearninggoalofhighrecognitionratebutonlymoderatelowfalsepositiverate.OnelimitationofAdaBoostarisesinthecontextofskewedexampledistributionandcascadeclassifiers:AdaBoostminimizestheclassificationerror,whichisnotguaranteedtoachievetheasymmetricnodelearninggoal.Inthispaper,weproposetousetheasymmetricAdaBoost(Asym-Boost)asamechanismtoaddresstheasymmetricnodelearninggoal.Moreover,thetwopartsoftheselectingfeaturesandformingensembleclassifiersaredecoupled,bothofwhichoccursimultaneouslyinAsymBoostandAdaBoost.FisherLinearDiscriminantAnalysis(FLDA)isusedontheselectedfea-turestolearnalineardiscriminantfunctionthatmaximizestheseparabilityofdataamongthedifferentclasses,whichwethinkcanimprovetherecognitionperformance.Theproposedalgorithmisdem-onstratedwithfacerecognitionusingaGaborbasedrepresentationontheFERETdatabase.Ex-perimentalresultsshowthattheproposedalgorithmyieldsbetterrecognitionperformancethanAdaBoostitself.

简介：Thispaperaddressesthehighdimensionsampleproblemindiscriminateanalysisundernonparametricandsupervisedassumptions.SincethereisakindofequivalencebetweentheprobabilisticdependencemeasureandtheBayesclassificationerrorprobability,weproposetouseaniterativealgorithmtooptimizethedimensionreductionforclassificationwithaprobabilisticapproachtoachievetheBayesclassifier.TheestimatedprobabilitiesofdifferenterrorsencounteredalongthedifferentphasesofthesystemarerealizedbytheKernelestimatewhichisadjustedinameansofthesmoothingparameter.Experimentresultssuggestthattheproposedapproachperformswell.

简介：一个模糊ARTMAP分类器为分类实验ofCBERS-2形象被采用。基本理论并且关于算法处理首先被介绍，在CBERS-2高决定形象上在Shihezi县与一个陆地使用分类实验列在后面。三个分类器被比较：最大的可能性分类器(MLC)，错误背繁殖(BP)分类器，和模糊ARTMAP分类器。比较比MLC和BP的高为模糊ARTMAP分类器显示出可比较地更好的结果，与9.9%和4.6%的全面分类精确性。结果也证明模糊ARTMAP分类器在在CBERS-2形象上识别赤裸的土壤有更好的洞察力。

简介：Simulatingbiologicalolfactoryneuralsystem,KⅢnetwork,whichisahigh-dimensionalchaoticneuralnetwork,isdesignedinthispaper.Differentfromconventionalartificialneuralnetwork,theKⅢnetworkworksinitschaotictrajectory.ItcansimulatenotonlytheoutputEEGwaveformobservedinelectrophysiologicalexperiments,butalsothebiologicalintelligenceforpatternclassification.Thesimulationanalysisandapplicationtotherecognitionofhandwritingnmeralsarepresentedhere.TheclassificationperformanceoftheKⅢnetworkatdifferentnoiselevelswasalsoinvestigated.

简介：Inthisstudy,aMulti-LayerBPneuralnetwork(MLBP)withdynamicthresholdsisemployedtobuildaclassifiermodel.Astothedesignoftheneuralnetworkstructure,theoreticalguidanceandplentifulexperimentsarecombinedtooptimizethehiddenlayers'parameterswhichincludethenumberofhiddenlayersandtheirnodenumbers.Theclassifierwithdynamicthresholdsisusedtostandardizetheoutputforthefirsttime,anditimprovestherobustnessofthemodeltoahighlevel.Finally,theclassifierisappliedtoforecastboxofficerevenueofamoviebeforeitstheatricalrelease.ThecomparisonresultswiththeMLPmethodshowthattheMLBPclassifiermodelachievesmoresatisfactoryresults,anditismorereliableandeffectivetosolvetheproblem.

简介：Recently,ithasbeenseenthattheensembleclassifierisaneffectivewaytoenhancethepredictionperformance.However,itusuallysuffersfromtheproblemofhowtoconstructanappropriateclassifierbasedonasetofcomplexdata,forexample,thedatawithmanydimensionsorhierarchicalattributes.Thisstudyproposesamethodtoconstructeanensembleclassifierbasedonthekeyattributes.Inadditiontoitshigh-performanceonprecisionsharedbycommonensembleclassifiers,thecalculationresultsarehighlyintelligibleandthuseasyforunderstanding.Furthermore,theexperimentalresultsbasedontherealdatacollectedfromChinaMobileshowthatthekey-attributes-basedensembleclassifierhasthegoodperformanceonbothoftheclassifierconstructionandthecustomerchurnprediction.

简介：

简介：AIM:Toinvestigateandcomparetheefficacyoftwomachine-learningtechnologieswithdeep-learning(DL)andsupportvectormachine(SVM)forthedetectionofbranchretinalveinocclusion(BRVO)usingultrawide-fieldfundusimages.METHODS:Thisstudyincluded237imagesfrom236patientswithBRVOwithamean±standarddeviationofage66.3±10.6yand229imagesfrom176non-BRVOhealthysubjectswithameanageof64.9±9.4y.Trainingwasconductedusingadeepconvolutionalneuralnetworkusingultrawide-fieldfundusimagestoconstructtheDLmodel.Thesensitivity,specificity,positivepredictivevalue(PPV),negativepredictivevalue(NPV)andareaunderthecurve(AUC)werecalculatedtocomparethediagnosticabilitiesoftheDLandSVMmodels.RESULTS:FortheDLmodel,thesensitivity,specificity,PPV,NPVandAUCfordiagnosingBRVOwas94.0%(95%CI:93.8%-98.8%),97.0%(95%CI:89.7%-96.4%),96.5%(95%CI:94.3%-98.7%),93.2%(95%CI:90.5%-96.0%)and0.976(95%CI:0.960-0.993),respectively.Incontrast,fortheSVMmodel,thesevalueswere80.5%(95%CI:77.8%-87.9%),84.3%(95%CI:75.8%-86.1%),83.5%(95%CI:78.4%-88.6%),75.2%(95%CI:72.1%-78.3%)and0.857(95%CI:0.811-0.903),respectively.TheDLmodeloutperformedtheSVMmodelinalltheaforementionedparameters(P<0.001).CONCLUSION:TheseresultsindicatethatthecombinationoftheDLmodelandultrawide-fieldfundusophthalmoscopymaydistinguishbetweenhealthyandBRVOeyeswithahighlevelofaccuracy.TheproposedcombinationmaybeusedforautomaticallydiagnosingBRVOinpatientsresidinginremoteareaslackingaccesstoanophthalmicmedicalcenter.

简介：Duetothefeaturesofthemulti-spectralimages,theresultwiththeusualmethodsbasedonthesupportvectormachine(SVM)andbinarytreeisnotsatisfactory.Inthispaper,afuzzySVMmulti-classclassifierwiththebinarytreeisproposedfortheclassificationofmulti-spectralimages.Theexperimentisconductedonamulti-spectralimagewith6bandswhichcontainsthreeclassesofterrains.Theexperimentalresultsshowthatthismethodcanimprovethesegmentationaccuracy.

简介：Inthiswork,ahardwareintrusiondetectionsystem(IDS)modelanditsimplementationareintroducedtoperformonlinereal-timetrafficmonitoringandanalysis.TheintroducedsystemgatherssomeadvantagesofmanyIDSs:hardwarebasedfromimplementationpointofview,networkbasedfromsystemtypepointofview,andanomalydetectionfromdetectionapproachpointofview.Inaddition,itcandetectmostofnetworkattacks,suchasdenialofservices(DoS),leakage,etc.fromdetectionbehaviorpointofviewandcandetectbothinternalandexternalintrudersfromintrudertypepointofview.GatheringthesefeaturesinoneIDSsystemgiveslotsofstrengthsandadvantagesofthework.Thesystemisimplementedbyusingfieldprogrammablegatearray(FPGA),givingamoreadvantagestothesystem.AC5.0decisiontreeclassifierisusedasinferenceenginetothesystemandgivesahighdetectionratioof99.93%.

简介：AbstractBackground:Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists’ efforts. We aimed to evaluate the classification sensitivity and specificity of deep learning models to classify skin tumors and psoriasis for Chinese population with a modest number of dermoscopic images.Methods:We developed a convolutional neural network (CNN) based on two datasets from a consecutive series of patients who underwent the dermoscopy in the clinic of the Department of Dermatology, Peking Union Medical College Hospital, between 2016 and 2018, prospectively. In order to evaluate the feasibility of the algorithm, we used two datasets. Dataset I consisted of 7192 dermoscopic images for a multi-class model to differentiate three most common skin tumors and other diseases. Dataset II consisted of 3115 dermoscopic images for a two-class model to classify psoriasis from other inflammatory diseases. We compared the performance of CNN with 164 dermatologists in a reader study with 130 dermoscopic images. The experts’ consensus was used as the reference standard except for the cases of basal cell carcinoma (BCC), which were all confirmed by histopathology.Results:The accuracies of multi-class and two-class models were 81.49% ± 0.88% and 77.02% ± 1.81%, respectively. In the reader study, for the multi-class tasks, the diagnosis sensitivity and specificity of 164 dermatologists were 0.770 and 0.962 for BCC, 0.807 and 0.897 for melanocytic nevus, 0.624 and 0.976 for seborrheic keratosis, 0.939 and 0.875 for the "others" group, respectively; the diagnosis sensitivity and specificity of multi-class CNN were 0.800 and 1.000 for BCC, 0.800 and 0.840 for melanocytic nevus, 0.850 and 0.940 for seborrheic keratosis, 0.750 and 0.940 for the "others" group, respectively. For the two-class tasks, the sensitivity and specificity of dermatologists and CNN for classifying psoriasis were 0.872 and 0.838, 1.000 and 0.605, respectively. Both the dermatologists and CNN achieved at least moderate consistency with the reference standard, and there was no significant difference in Kappa coefficients between them (P > 0.05).Conclusions:The performance of CNN developed with relatively modest number of dermoscopic images of skin tumors and psoriasis for Chinese population is comparable with 164 dermatologists. These two models could be used for screening in patients suspected with skin tumors and psoriasis respectively in primary care hospital.