A5 problem solving paradigm: A unified perspective and new results on RHT computing, mixture based learning, and evidence combination
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要In this paper(1), the roles of grid, granular, modular structures in density learning and Hough transform (HT) like object detection, as well as the corresponding typical approaches have been systematically reviewed. Featured by five essential mechanisms (namely, acquisition, assumption, accumulation, adaptation, and assessment), a general problem solving paradigm, shortly A5 paradigm, is elaborated to provide not only a unified perspective but also new results on Hough transform (HT) like object detection, mixture based learning (RPCL learning and multi-set modelling), and evidence combination.
著者Xu L
會議名稱IEEE International Conference on Granular Computing
會議開始日25.07.2005
會議完結日27.07.2005
會議地點Beijing
會議國家/地區中國
出版年份2005
月份1
日期1
出版社IEEE
頁次70 - 77
國際標準書號0-7803-9017-2
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

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