Binary Classification Based on Potentials

Author

Boczko, Erik M. ; Di Lullo, A. ; Young, Todd R.

Author_Institution

Biomed. Inf., Vanderbilt Univeristy Med. Center, Nashville, TN, USA

fYear

2009

fDate

15-17 June 2009

Firstpage

129

Lastpage

132

Abstract

We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard Support Vector Machine methods.

Keywords

bioinformatics; learning (artificial intelligence); medical computing; radial basis function networks; binary classification; distance weighted discrimination; machine learning; potential functions; radial basis function networks; Bioinformatics; Biomedical computing; Biomedical informatics; Collaboration; Joining processes; Machine learning; Mathematics; Proteomics; 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.2009.31

Filename

5159176

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2452805