981.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: svm<3>Microarray Gene Expression Data
发信站: 南京大学小百合站 (Tue Jun  4 18:42:25 2002), 站内信件

Support Vector Machine Classification of Microarray Gene Expression Data
We introduce a new method of functionally classifying genes using gene expres
sion
 data from DNA microarray hybridization experiments. The method is based 
on the theory of support vector machines (SVMs). We describe SVMs that use
 different similarity metrics, including a simple dot product of gene expression
 vectors, polynomial versions of the dot product, and a radial basis function
. The radial basis function SVM appears to provide superior performance in
 classifying functional classes of genes when compared to the other SVM simil
arity
 metrics. In addition, SVM performance is compared to four standard machine
 learning algorithms. SVMs have many features that make them attractive for
 gene expression analysis, including their flexibility in choosing a similarity
 function, sparseness of solution when dealing with large data sets, the 
ability to handle large feature spaces, and the ability to identify outliers
. 
Reference(s): 
Support Vector Machine Classification of Microarray Gene Expression Data 

M. Brown, W. Grundy, D. Lin, N. Cristianini C. Sugnet, M. Ares Jr., D. Haussler
 
University of California, Santa Cruz, 
technical report UCSC-CRL-99-09. 
Reference link(s): 
http://www.cse.ucsc.edu/research/compbio/genex/genex.tech.html 
Data link(s): 
http://www.cse.ucsc.edu/research/compbio/genex/
Entered by: Nello Cristianini <[e-mail]> - Friday,
 September 10, 1999 at 03:11:46 (PDT) 
Comments: SVMs outperformed all other classifers, when provided with a specif
ically
 designed kernel to deal with very imbalanced data. 
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