Semi-supervised support vector machines

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 368-374

  Bennett, Kristin P ^a   Demiriz, Ayhan ^b  

^a Research Rensselaer Polytechnic Institute   (United States)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIOPHAGES; INTEGER PROGRAMMING; PROBLEM SOLVING;

CLASSIFICATION FUNCTIONS; COMPUTATIONAL RESULTS; INDUCTIVE LEARNING; LINEAR SUPPORT VECTOR MACHINES; MISCLASSIFICATION ERROR; MIXED-INTEGER PROGRAMS; STATISTICAL LEARNING THEORY; TRADITIONAL APPROACHES;

SUPPORT VECTOR MACHINES;

EID: 84898958346     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. K. P. Bennett and E. J. Bredensteiner. Geometry in learning. In C. Gorini, E. Hart, W. Meyer, and T. Phillips, editors, Geometry at Work, Washington, D.C., 1997. Mathematical Association of America. To appear.
2. K. P. Bennett and O. L. Mangasarian. Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Soßware, 1:23-34, 1992.
3. K. P. Bennett, D. H. Wu, and L. Auslender. On support vector decision trees for database marketing. R.P.I. Math Report No. 98-100, Rensselaer Polytechnic Institute, Troy, NY, 1998.
4. A.M. Bensaid, L.O. Hall, J.C. Bezdek, and L.P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29(5):859-871, 199.
5. P. S. Bradley and O. L. Mangasarian. Feature selection via concave minimization and support vector machines. Technical Report Mathematical Programming Technical Report 98-03, University of Wisconsin-Madison, 1998. To appear in ICML-98.
6. P. S. Bradley and O. L. Mangasarian. Massive data discrimination via linear support vector machines. Technical Report Mathematical Programming Technical Report 98-05, University of Wisconsin-Madison, 1998. Submitted for publication.
7. E. J. Bredensteiner and K. P. Bennett. Feature minimization within decision trees. Computational Optimization and Applications, 10:110-126, 1997.
8. C. J. C Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998. to appear.
9. C. Cortes and V. N. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
10. CPLEX Optimization Incorporated, Incline Village, Nevada. Using the CPLEX Callable Library, 1994.
11. R. Fourer, D. Gay, and B. Kernighan. AMPL A Modeling Language for Mathematical Programming. Boyd and Frazer, Danvers, Massachusetts, 1993.
12. T. T. Fries and R. Harrison Fries. Linear programming support vector machines for pattern classification and regression estimation: and the sr algorithm. Research report 706, University of Sheffield, 1998.
13. O. L. Mangasarian. Parsimonious least norm approximation. Technical Report Mathematical Programming Technical Report 97-03, University of Wisconsin-Madison, 1997. To appear in Computational Optimization and Applications.
14. P.M. Murphy and D.W. Aha. UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, California, 1992.
15. S. Odewahn, E. Stockwell, R. Pennington, R Humphreys, and W Zumach. Automated star/galaxy discrimination with neural networks. Astronomical Journal, 103(1):318-331, 1992.
16. E. Osuna, R. Freund, and F. Girosi. Support vector machines: Training and applications. AI Memo 1602, Maassachusets Institute of Technology, 1997.
17. J. Piatt. Sequentional minimal optimization: A fast algorithm for training support vector machines. Technical Report Technical Report 98-14, Microsoft Research, 1998.
18. M. Vaidyanathan, R.P. Velthuizen, P. Venugopal, L.P. Clarke, and L.O. Hall. Tumor volume measurements using supervised and semi-supervised mri segmentation. In Artificial Neural Networks in Engineering Conference, AN-NIE(1994), 1994.
19. V. N. Vapnik. Estimation of dependencies based on empirical Data. Springer, New York, 1982. English translation, Russian version 1979.
20. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.
21. V. N. Vapnik and A. Ja. Chervonenkis. Theory of Pattern Recognition. Nauka, Moscow, 1974. In Russian.