2.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: svm<2>Breast cancer diagnosis and prognosis
发信站: 南京大学小百合站 (Tue Jun  4 18:41:38 2002), 站内信件

Breast cancer diagnosis and prognosis
Support vector machines have been applied to breast cancer diagnosis and 
prognosis. The Wisconsin breast cancer dataset contains 699 patterns with
 10 attributes for a binary classification task (the tumor is malignant or
 benign). 
Reference(s): 
O. L. Mangasarian, W. Nick Street and W. H. Wolberg: ``Breast cancer diagnosis
 and prognosis via linear programming", Operations Research, 43(4), July-
August 1995, 570-577. 
P. S. Bradley, O. L. Mangasarian and W. Nick Street: ``Feature selection 
via mathematical programming", INFORMS Journal on Computing 10, 1998, 209
-217. 

P. S. Bradley, O. L. Mangasarian and W. Nick Street: ``Clustering via concave
 minimization", in ``Advances in Neural Information Processing Systems -9
-", (NIPS*96), M. C. Mozer and M. I. Jordan and T. Petsche, editors, MIT 
Press, Cambridge, MA, 1997, 368-374. 

T.-T. Friess; N. Cristianini; C. Campbell. 
The kernel adatron algorithm: a fast and simple learning procedure for support
 vector machines. 
15th Intl. Conf. Machine Learning, Morgan Kaufman Publishers. 
1998. 

Reference link(s): 
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/94-10.ps 
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/95-21.ps 
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/96-03.ps 
http://svm.first.gmd.de/papers/FriCriCam98.ps.gz 
Data link(s): 
WDBC: Wisconsin Diagnostic Breast Cancer Database 
BC: Wisconsin Prognostic Breast Cancer Database
Entered by: Prof. Olvi L. Mangasarian <[e-mail]> - Thursday, September
 09, 1999 at 15:25:50 (PDT) 
Modified by: Isabelle Guyon <[e-mail]> - Monday, September 20
, 1999 at 9:30 (PDT) 
Comments: Mangasarian et al use a linear programming formulation underlying
 that can be interpreted as an SVM. Their system (XCYT) is a highly accurate
 non-invasive breast cancer diagnostic program currently in use at University
 of Wisconsin Hospital. Friess et al report that the Wisconsin breat cancer
 dataset has been extensively studied. Their system, which uses Adatron SVMs
, has 99.48% success rate, compared to 94.2% (CART), 95.9% (RBF), 96% (linear
 discriminant), 96.6% (Backpropagation network), all results reported elsewhere
 in the literature. 

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