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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).

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简介：AbstractMachine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

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

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简介：近年来,多媒体网络教育的普及,为社会大众开辟了一个多元化的学习途径,E-Leaming作为一种先进的学习方式,以其快速、高效的特点而越来越被大众接受,它的出现对传统的学校教育和学习型社会将产生重要影响.

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简介：这份报纸基于sum-of-processing-time与更一般的学习效果处理单个机器的安排问题。在这研究，一个工作的处理时间被减少定义的sum-of-processing-time-based学习效果工具处理在顺序先于它的工作的时间的全部的正常工作。甚至与sum-of-processing-time-based的介绍，到工作处理的学习效果预定的结果表演，单个机器的makespan最小化问题仍然保持polynomially可解决。一个全部的结束时间最小化问题的最佳的时间表的曲线关于处理时间的工作正常是塑造V的。

简介：一张链图允许指导并且未受指导的边，并且包含二的内在的数学性质。学习图形的模型的一个重要方法是使用得分标准测量图结构多好适合数据。在这份报纸，我们为基于Kullback鈥揕eibler距离学习链图的现在的得分标准。它是20张相等的、也就是说相等的链图获得一样的分数，因此执行平均的模型选择和模型能被用来。关键词链图-Markov等价-得分标准先生(2000)题目分类62B10-62H99由NNSFC(39930160)支持了；部分由BNU青春基础(104951)支持了

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简介：Asiswellknown,somepeoplearemoresuccessfulthanothersinlearning.Thisdifferentlevelsofachievementmaybeattributedtovariablesassociatedwiththelearner.Inrecentyearstherehasbeenextensiveresearchintoaspectsofdifferencesinlearningasecondlanguage.Thispaperbrieflyreviewsanddiscussesthemajorparametersofthedifferencesamongindividu-alswhichresearchstudiesindicatemayinfluencethesuccessofsecondlanguagelearning,citingsixareasofinterest:age,intel-ligence,cognitivestyles,personality,motivationandattitude.