Document Detail


Statistical pattern classification: A review and some current problemsparadigms.
MedLine Citation:
PMID:  20331185     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Unsupervised clustering is the ability to automatically partition a set of data patterns into meaningful groups without prior knowledge of the groups or their number. Supervised classification is the ability to automatically recognize a data pattern as an instance from one of a known set of classes. These problems are fundamental to a variety of application domains, including scientific (e.g., bioinformatics), engineering (e.g., speech recognition), business (e.g., marketing and document clustering), and military (target detection). In this talk, we first review the basic supervised and unsupervised classification problems, standard solution methodologies, and how to characterize their performance. We then discuss some recent problem variants and associated paradigms, including semisupervised learning, unsupervised clustering in high-dimensional feature spaces, and decision fusion techniques.
Authors:
David J Miller
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Journal of the Acoustical Society of America     Volume:  127     ISSN:  1520-8524     ISO Abbreviation:  J. Acoust. Soc. Am.     Publication Date:  2010 Mar 
Date Detail:
Created Date:  2010-03-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7503051     Medline TA:  J Acoust Soc Am     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2024     Citation Subset:  IM    
Affiliation:
Dept. of Elec. Eng., The Penn State Univ., Rm. 227-C EE West Bldg., Univ. Park, PA 16802.
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