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by admin last modified 2008-11-11 09:39
Publications in the database on Kernel-Machines.Org

This folder holds the following references to publications, sorted by year and author.

There are 357 references in this bibliography folder.

Tsuda, K (1999).
Support vector classifier with asymmetric kernel function
In: Proceedings of ESANN'99, ed. by M. Verleysen, pp. 183 – 188, Brussels, D Facto.

Tsuda, K (1999).
Optimal Hyperplane Classifier with Adaptive Norm
ETL.

Vapnik, V (1999).
Three Remarks on the Support Vector Method of Function Estimation
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 25–42, Cambridge, MA, MIT Press.

Vapnik, V and Mukherjee, S (1999).
Support Vector Method for Multivariate Density Estimation
In: Neural Information Processing Systems.

Vivarelli, F and Williams, CK (1999).
Discovering hidden features with Gaussian processes regression
In: Advances in Neural Information Processing Systems 11, ed. by Kearns, M. S. and Solla, S. A. and Cohn, D. A., MIT Press.

Wahba, G (1999).
Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 69–88, Cambridge, MA, MIT Press.

Weston, J, Gammerman, A, Stitson, M, Vapnik, V, Vovk, V, and Watkins, C (1999).
Support Vector Density Estimation
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 293–306, Cambridge, MA, MIT Press.

Williamson, R, Smola, A, and Sch\"olkopf, B (1999).
Entropy numbers, operators and support vector kernels
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 127–144, Cambridge, MA, MIT Press.

Zhang, X (1999).
Using Class-Center Vectors to Build Support Vector Machines
In: Proceedings of NNSP'99.

Anguita, D, Ridella, S, and Rovetta, S (1998).
Circuital implementation of support vector machines
Electronics Letters, 34(16).

Bennet, K and Demiriz, A (1998).
Semi-Supervised Support Vector Machines
In: Advances in Neural Information Processing Systems 11, pp. 368–374, MIT Press.

Bennett, KP, Hui, D, and Auslender, L (1998).
On Support Vector Decision Trees for Database Marketing
Rensselaer Polytechnic Institute, Department of Mathematical Sciences(Math Report No. 98-100), Troy, NY 12180.

Bradley, P (1998).
Mathematical Programming Approaches to Machine Learning and Data Mining
PhD thesis, University of Wisconsin, Computer Sciences Department, Madison, WI, USA.

Bradley, P and Mangasarian, O (1998).
Massive Data Discrimination via Linear Suppport Vector Machines
University of Wisconsin Madison, Mathematical Programming Technical Report(98-05).

Bradley, PS and Mangasarian, OL (1998).
Feature Selection via Concave Minimization and Support Vector Machines
In: Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), ed. by J. Shavlik, pp. 82-90, San Francisco, California, Morgan Kaufmann.

Bradley, PS, Fayyad, UM, and Mangasarian, OL (1998).
Data Mining: Overview and Optimization Opportunities
University of Wisconsin, Computer Sciences Department, Madison.

Brown, M, Lewis, HG, and Gunn, SR (1998).
Linear spectral mixture models and support vector machines for remote sensing
IEEE Trans Geoscience and Remote Sensing, submitted.

Bugmann, G (1998).
Classification using Networks of Normalized Radial Basis Functions
In: International Conference on Advances in Pattern Recognition ICAPR'98, ed. by S. Singh, pp. 435-444, London, Springer.

Burges, CJ (1998).
A Tutorial on Support Vector Machines for Pattern Recognition
Knowledge Discovery and Data Mining, 2(2).

Cherkassky, V and Mulier, F (1998).
Learning from Data
Wiley, New York.

Cristianini, N, Campbell, C, and Shawe–Taylor, J (1998).
Multiplicative Updatings for Support-Vector Learning
Royal Holloway College, NeuroCOLT Technical Report(NC-TR-98-016), University of London, UK.

Cristianini, N, Campbell, C, and Shawe–Taylor, J (1998).
Dynamically Adapting Kernels in Support Vector Machines
Royal Holloway College, NeuroCOLT Technical Report(NC-TR-98-017), University of London, UK.

Frieß, T, Cristianini, N, and Campbell., C (1998).
The Kernel Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines.
In: 15th Intl. Conf. Machine Learning, Morgan Kaufmann Publishers.

Goldberg, PW, Williams, CK, and Bishop, CM (1998).
Regression with Input-dependent Noise: A Gaussian Process Treatment
In: Advances in Neural Information Processing Systems 10, ed. by Jordan, M. I. and Kearns, M. J. and Solla, S. A., MIT Press, Cambridge, MA.

Hearst, MA, Sch\"olkopf, B, Dumais, S, Osuna, E, and Platt, J (1998).
Trends and Controversies - Support Vector Machines.
IEEE Intelligent Systems, 13(4):18-28.

 

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