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Volumn 4183 LNCS, Issue , 2006, Pages 162-171

Clustering approach using belief function theory

Author keywords

Belief function theory; Clustering; K modes method; Machine learning; Uncertainty

Indexed keywords

ALGORITHMS; DATA HANDLING; DATA STRUCTURES; LEARNING SYSTEMS; MATHEMATICAL MODELS;

EID: 33750225286     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper
Times cited : (37)

References (18)
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    • Assessing sensor reliability for multisensor data fusion within the transferable belief model
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    • Elouedi, Z.1    Mellouli, K.2    Smets, Ph.3
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    • Huang, Z.1
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    • J.R. Quinlan, Learning efficient classification and their application to chess end games. In R. S. Michalski, J. G. Carbonell, and T. M. Michell (Eds.), Machnie Learning: An artificial intelligence approach, 463-482. Morgan Kaufmann, 1983.
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    • D.E. Rumelhart and J.L. McClelland, editors. Cambridge, MA: MIT Press
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    • The transferable belief model for quantified belief representation
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    • Ph. Smets: The transferable belief model for quantified belief representation. In D. M. Gabbay and P. Smets (Eds.), Handbook of defeasible reasoning and uncertainty management systems, Vol 1, 267-301, 1998b.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.