Bibliography

1

D. P. Bertsekas.
Nonlinear Programming.
Athena Scientific, Belmont, MA, second edition, 1999.

2

P. S. Bradley and O. L. Mangasarian.
Feature selection via concave minimization and support vector machines.
In J. Shavlik, editor, Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), pages 82-90, San Francisco, California, 1998. Morgan Kaufmann.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-03.ps .

3

P. S. Bradley and O. L. Mangasarian.
Massive data discrimination via linear support vector machines.
Optimization Methods and Software, 13:1-10, 2000.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-03.ps .

4

V. Cherkassky and F. Mulier.
Learning from Data - Concepts, Theory and Methods.
John Wiley & Sons, New York, 1998.

5

N. Cristianini and J. Shawe-Taylor.
An Introduction to Support Vector Machines.
Cambridge University Press, Cambridge, 2000.

6

M. C. Ferris and T. S. Munson.
Interior point methods for massive support vector machines.
Technical Report 00-05, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, May 2000.
ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/00-05.ps.

7

G. H. Golub and C. F. Van Loan.
Matrix Computations.
The John Hopkins University Press, Baltimore, Maryland, 3rd edition, 1996.

8

T. K. Ho and E. M. Kleinberg.
Checkerboard dataset, 1996.
http://www.cs.wisc.edu/math-prog/mpml.html.

9

ILOG, Inc.
ILOG CPLEX 6.5 Reference Manual.
ILOG CPLEX Division, Incline Village, Nevada, 1999.

10

T. Joachims.
SVM$^{light}$, 1998.
http://ais.gmd.de/~thorsten/svm_light.

11

T. Joachims.
Making large-scale support vector machine learning practical.
In Schölkopf et al. [25], pages 169-184.

12

L. Kaufman.
Solving the quadratic programming problem arising in support vector classification.
In Schölkopf et al. [25], pages 147-167.

13

Y.-J. Lee and O. L. Mangasarian.
RSVM: Reduced support vector machines.
In Proceedings of the First SIAM International Conference on Data Mining, 2001.
To appear. ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/00-07.ps.

14

Yuh-Jye Lee and O. L. Mangasarian.
SSVM: A smooth support vector machine.
Computational Optimization and Applications, 2000.
To appear. ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/99-03.ps.

15

O. L. Mangasarian.
Nonlinear Programming.
SIAM, Philadelphia, PA, 1994.

16

O. L. Mangasarian.
Generalized support vector machines.
In A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 135-146, Cambridge, MA, 2000. MIT Press.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-14.ps.

17

O. L. Mangasarian and D. R. Musicant.
Successive overrelaxation for support vector machines.
IEEE Transactions on Neural Networks, 10:1032-1037, 1999.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-18.ps.

18

O. L. Mangasarian and D. R. Musicant.
Active support vector machine classification.
Advances in Neural Information Processing Systems (NIPS 2000), 2000.
To appear. ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/00-04.ps.

19

O. L. Mangasarian and D. R. Musicant.
Data discrimination via nonlinear generalized support vector machines.
In M. C. Ferris, O. L. Mangasarian, and J.-S. Pang, editors, Complementarity: Applications, Algorithms and Extensions, pages 233-251, Dordrecht, January 2001. Kluwer Academic Publishers.
ftp://ftp.cs.wisc.edu/math-prog/tech-reports/99-03.ps.

20

O. L. Mangasarian and J. Ren.
New improved error bounds for the linear complementarity problem.
Mathematical Programming, 66:241-255, 1994.

21

O. L. Mangasarian and M. V. Solodov.
Nonlinear complementarity as unconstrained and constrained minimization.
Mathematical Programming, Series B, 62:277-297, 1993.

22

P. M. Murphy and D. W. Aha.
UCI repository of machine learning databases, 1992.
http://www.ics.uci.edu/~mlearn/MLRepository.html.

23

D. R. Musicant.
NDC: Normally Distributed Clustered Datasets, 1998.
http://www.cs. wisc.edu/~musicant/data/ndc.

24

J. Platt.
Sequential minimal optimization: A fast algorithm for training support vector machines.
In Schölkopf et al. [25], pages 185-208.
http://www.research.microsoft.com/~jplatt/smo.html.

25

B. Schölkopf, C. Burges, and A. Smola.
Advances in Kernel Methods: Support Vector Machines.
MIT Press, Cambridge, MA, 1999.

26

The MathWorks, Inc.
MATLAB User's Guide.
The MathWorks, Inc., Natick, MA 01760, 1994-2001.

27

The MathWorks, Inc.
MATLAB Solution Number 1913: Can one session of MATLAB take advantage of multiple processors on the same PC or UNIX machine?, 2000.
http://www.mathworks.com/support/solutions/data/1913.shtml.

28

V. N. Vapnik.
The Nature of Statistical Learning Theory.
Springer, New York, 1995.