摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究-支持向量机是一篇报道的主题。根据NewsRx编辑在墨西哥塔巴斯科的新闻报道，研究表明，"我们考虑通过一对一(OVO)或一对一(OVR)方法对二元判别方法的多分类扩展，重点是通过线性平均差(MD)、支持向量机(SVM)、最大数据堆积(MDP)和通过OVO的距离加权判别（DWD）对二元判别方法的扩展，以及通过OVR对MD的多分类扩展。在高维、低样本量(HDLSS)数据的背景下,利用新数据点正确分类概率,描述了当数据维数增加、样本量固定时,OVO-MD、OVO-SVM、OVO-MDP和OVO-ODWD的渐近行为,得到了当维数接近无穷大时正确分类概率S收敛到1的充分条件.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning - Sup port Vector Machines is the subject of a report. According to news reporting out of Tabasco, Mexico, by NewsRx editors, research stated, “We consider multicateg ory extensions of binary discrimination methods via one-versus-one (OVO) or one- versus-rest (OVR) methodologies, focusing on extensions of the binary classifica tion by linear mean difference (MD), support vector machine (SVM), maximal data piling (MDP), and distance weighted discrimination (DWD) via OVO, and the multic ategory extension of MD via OVR, in the context of high-dimensional and low samp le size (HDLSS) data. The asymptotic behavior of OVO-MD, OVO-SVM, OVO-MDP and OV ODWD is described when the dimension of the data increases and the sample size is fixed, in terms of the probabilities of correct classification of a new data point, finding sufficient conditions for the correct classification probabilitie s to converge to one as the dimension approaches infinity.”
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