文章基本信息

标题：A Randomized Approximation Convex Hull Algorithm for High Dimensions
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作者：Antonio Ruano ; Hamid Reza Khosravani ; Pedro M. Ferreira 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：10
页码：123-128
DOI：10.1016/j.ifacol.2015.08.119
语种：English
出版社：Elsevier
摘要：AbstractThe accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models that covers the whole input ranges in which they will be employed. The convex hull algorithm is applied as a method for data selection; however the use of conventional implementations of this method in high dimensions, due to its high complexity, is not feasible. In this paper, we propose a randomized approximation convex hull algorithm which can be used for high dimensions in an acceptable execution time.
关键词：KeywordsConvex HullData Selection ProblemClassificationRegressionNeural NetworksSupport Vector Machines