DocumentCode
2452839
Title
Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines
Author
Boczko, Erik M. ; Xie, Minhui ; Wu, Di ; Young, Todd
Author_Institution
Dept. Biomed. Inf., Vanderbilt Univ. Med. Center, Nashville, TN, USA
fYear
2009
fDate
15-17 June 2009
Firstpage
139
Lastpage
143
Abstract
We compare methods based on the Signed Distance Function (SDF) a new tool for binary classification with standard Support Vector Machine (SVM) methods. We demonstrate on several sets of micro-array data that the performance of the SDF based methods can match or exceed that of SVM methods.
Keywords
bioinformatics; support vector machines; binary classification; biomedical informatics; microarray data; signed distance functions; support vector machines; Bioinformatics; Biomedical informatics; Biomedical measurements; Classification algorithms; Diseases; Genomics; Proteomics; Robustness; Support vector machine classification; Support vector machines; Machine Learning; Microarray Data;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics, 2009. OCCBIO '09. Ohio Collaborative Conference on
Conference_Location
Cleveland, OH
Print_ISBN
978-0-7695-3685-9
Type
conf
DOI
10.1109/OCCBIO.2009.30
Filename
5159178
Link To Document