简介:上季季后赛时期为了照顾开刀的女儿,费舍尔(DerekFisher)几度奔波纽约医院与盐湖城的球场之间,虽然他几次及时出现能给爵士队带来额外的勇气,但他很清楚这样的情况并不能长久,为了家人着想,他还是得做些
简介:摘要 由于矩阵的初等变换和初等矩阵都有“初等”二字,所以非常容易将二者混为一谈.此文的目的在于解释这两个概念的区别,同时也介绍它们的关系.在对矩阵进行运算时,我们可对其进行类似于行列式的行(列)变换或数乘运算等,即矩阵的初等变换.为了搞清楚变换后的矩阵所具有的特性,也为了说明矩阵的初等变换的意义,我们引入初等矩阵的概念.其实初等矩阵就是单位矩阵经矩阵的初等变换后所得的矩阵.具体内容见下文简述.
简介:WeprovideanewexpressionofthequantumFisherinformation(QFI)forageneralsystem.Utilizingthisexpression,theQFIforanon-fullrankdensitymatrixisonlydeterminedbyitssupport.Thisexpressioncanbringconvenienceforaninfinite-dimensionaldensitymatrixwithafinitesupport.Besides,amatrixrepresentationoftheQFIisalsogiven.
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:Anoveltextindependentspeakeridentificationsystemisproposed.Intheproposedsystem,the12-orderperceptuallinearpredictivecepstrumandtheirdeltacoefficientsinthespanoffiveframesareextractedfromthesegmentedspeechbasedonthemethodofpitchsynchronousanalysis.TheFisherratiosoftheoriginalcoefficientsthenbecalculated,andthecoefficientswhoseFisherratiosarebiggerareselectedtoformthe13-dimensionalfeaturevectorsofspeaker.TheGaussianmixturemodelisusedtomodelthespeakers.TheexperimentalresultsshowthattheidentificationaccuracyoftheproposedsystemisobviouslybetterthanthatofthesystemsbasedonotherconventionalcoefficientslikethelinearpredictivecepstralcoefficientsandtheMel-frequencycepstralcoefficients.
简介:在古典统计,菲希尔信息在它是在概率密度的空间公制的实质上唯一的单调Riemannian的意义是唯一的。Inquantum理论,这唯一垮掉,并且有菲希尔信息,二个特别版本由他们的直觉、参考的意义在之中区分自己的许多自然的量类似物:首先,它在斜信息的起源在量测量的上下文在1963介绍了byWigner和Yanase,并且是经由密度操作员的方形的根的deSned。第二从Helstrom产生“s在1967量察觉学习,并且经由对称的对数的衍生物被定义。这篇论文的目的是比较量菲希尔信息的这二个版本,并且建立联系他们的二参考不平等。
简介:摘要:数据作为现代社会的重要生产要素,成为企业竞争中关键的一环,将个人信息权作为具体人格权来进行保护,忽视了个人信息、数据的财产属性,将个人信息从财产利益中剥离出来明显与各企业、平台不遗余力地搜刮用户信息以加强竞争优势或获取更多利润的目的相矛盾。个人信息、数据的定义的至关重要,笔者肯定个人信息的人格利益属性,本文将从个人信息、数据本身出发,采用霍菲尔德法律概念矩阵,厘清个人信息、数据各方主体的关系,探讨个人信息的财产性质,以期达到多重保护的目的。