简介：一个新歧管的学习方法，叫的增长排列方法(IAM)，与内在的低维数为高维的数据的非线性的维数减小被建议。主要想法是逐渐地排列输入数据patch-by-patch的低维的坐标反复地产生全部数据集的表示。方法由二主要的步，增长的步和排列步组成。增长的步逐渐地寻找邻居补丁在下一步被排列，并且排列步反复地排列寻找产生全部数据集的embeddings的邻居补丁的低维的坐标。与存在相比歧管的学习方法，建议方法在几统治方面：高效率，容易的out-of-sample扩展，很好公制保存，并且本地最小问题避免。所有这些性质被在合成、真实的数据集上执行的一系列实验支持。另外，建议方法的计算复杂性被分析，并且它的效率理论上被说服并且试验性地示威了。

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简介：Thepurposeofthisstudywastounderstandtheeffectofimplementingmultipleintelligence(MI)-basedinstructiononthesciencelearningmotivationandlearningachievementof7thgradejuniorhighstudents.Thisquasi-experimentalresearchstudyutilizedquestionnairesandtestsofsciencelearningmotivationandlearningachievement,andexaminedcollecteddatathroughuseofthestatisticalpackageforthesocialsciences(SPSS).Ananalysisofcovariance(ANCOVA)andat-testwereperformedinordertoexplorethedifferencesbetweenthesciencelearningmotivationsandlearningachievementsoftheexperimentalgroup(N=39),whoweresubjectedtoteachingbasedonMItheory,andthecontrolgroup(N=36),whoweresubjectedtotraditionallectures.Theresultsofthestudyshowthat,afterimplementingMI-basedinstruction,thestudentsintheexperimentalgroupsignificantlyoutperformedthoseinthecontrolgroupintermsofsciencelearningmotivation,sciencelearningvalue,andactivelearningstrategies,whilethestudentswithlowlearningachievementshowinganimprovementintheirlearningattitudethatallowedthemtodevelopapositivelearningattitudetowardbiologyclassesandraisedtheirclassparticipationandpreferenceforbiologyclasses.

简介：Thearticleexaminestheworldexperienceofe-learningaswellasdistanceeducationtechnologieswithintheeducationprocessorganizationonhigherandpost-highereducationprograms.Therehavebeenlistedtheresultsofthemostpopulare-leamingplatformsanalysis.Furthermore,therehavebeenlookedthroughthecorelegislativebackgroundofthedevelopmentofthementionedtechnologiesinRussiaandworldwideamongtheuniversities,specializedinseafarerstraining.TherehavebeenalsodrawnupthepointsoftheAdmiralMakarovStateUniversityofMaritimeandInlandShipping(AdmiralMakarovSUMIS)designofthedistanceeducationsystemLMS“FARWATER”incompliancewiththeInternationalConventiononStandardsofTraining,CertificationandWatchkeepingforSeafarers(STCWConvention).Thepracticalapplicationofdistanceeducationsystemtotheadvancedprofessionaltraininghasbeendiscussedinthearticle.

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简介：IntroductionTheproliferationofapproachesandmethodsisaprominentcharacteristicofcontemporaryESLorEFLteaching.FromtheGrammarTranslationMethod,theDirectMethod,SituationalLanguageTeaching,theAudiolingualMethodtoCommunicativeLanguageTeaching,TotalPhysicalResponse,theSilentWay,CommunityLanguageLearning,theNaturalApproachandSuggestopedia,thediscussionhaslastedformorethanacentury.However,thewidevarietyofmethodorapproachoptionsconfusesratherthancomforts,becausetheyseemtoholdverydifferentviewsofwhatlanguageisandhowalanguageislearned(Richards&Rodgers,1991).Thisconfusingsituationismainlycausedbythemythofthehu-

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简介：Inthispaper,weproposetwoweightedlearningmethodsfortheconstructionofsinglehiddenlayerfeedforwardneuralnetworks.Bothmethodsincorporateweightedleastsquares.Ourideaistoallowthetraininginstancesnearertothequerytoofferbiggercontributionstotheestimatedoutput.Byminimizingtheweightedmeansquareerrorfunction,optimalnetworkscanbeobtained.Theresultsofanumberofexperimentsdemonstratetheeffectivenessofourproposedmethods.

简介：语言学习是一个长期的过程，也是一个艰苦的过程，更是一个快乐的、收获的过程。本文给大家提供了十条建议，几乎涵盖了语言学习的各个方面，相信会给我们很大的启发。

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简介：ThisessaymainlydealswiththeeffectualwaystomotivatestudentsintheirEnglishlearning,themotivationandteachers'roleinmotivatingstudents.Theauthordoeshope,throughthiskindofstudying,moreandmoreEnglishteacherscometorealizetheimportanceofmotivationanddosomeresearchtoimprovestudents'Englishlevel.

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简介：ThetraditionalGaussianMixtureModel(GMM)forpatternrecognitionisanunsupervisedlearningmethod.Theparametersinthemodelarederivedonlybythetrainingsamplesinoneclasswithouttakingintoaccounttheeffectofsampledistributionsofotherclasses,hence,itsrecognitionaccuracyisnotidealsometimes.ThispaperintroducesanapproachforestimatingtheparametersinGMMinasupervisingway.TheSupervisedLearningGaussianMixtureModel(SLGMM)improvestherecognitionaccuracyoftheGMM.Anexperimentalexamplehasshownitseffectiveness.TheexperimentalresultshaveshownthattherecognitionaccuracyderivedbytheapproachishigherthanthoseobtainedbytheVectorQuantization(VQ)approach,theRadialBasisFunction(RBF)networkmodel,theLearningVectorQuantization(LVQ)approachandtheGMM.Inaddition,thetrainingtimeoftheapproachislessthanthatofMultilayerPerceptrom(MLP).