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


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AbstractIn 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.
All Author(s) ListXu L
Name of ConferenceIEEE International Conference on Granular Computing
Start Date of Conference25/07/2005
End Date of Conference27/07/2005
Place of ConferenceBeijing
Country/Region of ConferenceChina
Year2005
Month1
Day1
PublisherIEEE
Pages70 - 77
ISBN0-7803-9017-2
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

Last updated on 2020-30-03 at 00:04