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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.

Hsu, C and Lin, C (1999).
A simple decomposition method for support vector machines
National Taiwan University.

Jaakkola, T and Haussler, D (1999).
Exploiting generative models in discriminative classifiers
In: Advances in Neural Information Processing Systems 11.

Jaakkola, TS and Haussler, D (1999).
Probabilistic Kernel Regression Models
In: Proceedings of the 1999 Conference on AI and Statistics.

Joachims, T (1999).
Making Large–Scale SVM Learning Practical
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 169–184, Cambridge, MA, MIT Press.

Joachims, T (1999).
Transductive Inference for Text Classification using Support Vector Machines
In: International Conference on Machine Learning (ICML), Bled, Slovenia.

Joachims, T (1999).
Aktuelles Schlagwort: Support Vector Machines
K\"unstliche Intelligenz, 4.

Jordaan, E (1999).
Development of Adaptive, Online Soft Sensors
Master thesis, Stan Ackermans Institute, Eindhoven.

Kaufmann, L (1999).
Solving the Quadratic Programming problem arising in Support Vector classification
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 147–168, Cambridge, MA, MIT Press.

Keerthi, S, Shevade, S, Bhattacharyya, C, and Murthy, K (1999).
A fast iterative nearest point algorithm for support vector machine classifier design
Dept of CSA, IISc, Bangalore, India.

Keerthi, S, Shevade, S, Bhattacharyya, C, and Murthy, K (1999).
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Dept of CSA, IISc, Bangalore, India.

Kreßel, U (1999).
Pairwise Classification and Support Vector Machines
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 255–268, Cambridge, MA, MIT Press.

Lee, Y and Mangasarian, OL (1999).
SSVM: A Smooth Support Vector Machine for Classification
University of Wisconsin, Data Mining Institute, Madison.

Lin, C (1999).
Formulations of Support Vector Machines: a Note from an Optimization Point of View
National Taiwan University, Dept. of Computer Science.

Lin, Y (1999).
Support Vector Machines and the Bayes Rule in Classification
University of Wisconsin-Madison, Department of statistics technical report(1014).

M\"uller, K, Smola, A, R\"atsch, G, Sch\"ol\-kopf, B, Kohlmorgen, J, and Vapnik, V (1999).
Predicting Time Series with Support Vector Machines
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 243–254, Cambridge, MA, MIT Press.

Mangasarian, OL and Musicant, D (1999).
Massive Support Vector Regression
University of Wisconsin, Data Mining Institute, Madison.

Mangasarian, OL and Musicant, DR (1999).
Data Discrimination via Nonlinear Generalized Support Vector Machines
University of Wisconsin, Computer Sciences Department, Madison, WI, USA.

Mangasarian, OL and Musicant, DR (1999).
Successive Overrelaxation for Support Vector Machines
IEEE Transactions on Neural Networks, 10:1032-1037.

Mangasarian, OL and Musicant, DR (1999).
Robust Linear and Support Vector Regression
Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin.

Mattera, D and Haykin, S (1999).
Support Vector Machines for Dynamic Reconstruction of a Chaotic System
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 211–242, Cambridge, MA, MIT Press.

Mika, S, Rätsch, G, Sch\"olkopf, B, Smola, A, Weston, J, and Müller, K (1999).
Invariant Feature Extraction and Classification in Kernel Spaces
In: Advances in Neural Information Processing Systems 12, Cambridge, MA, MIT Press.

Mika, S, Rätsch, G, Weston, J, Schölkopf, B, and Müller, K (1999).
Fisher Discriminant Analysis with Kernels
In: Neural Networks for Signal Processing IX, ed. by Y.-H. Hu and J. Larsen and E. Wilson and S. Douglas, pp. 41–48, IEEE.

Mika, S, Sch\"olkopf, B, Smola, A, M\"uller, K, Scholz, M, and R\"atsch, G (1999).
Kernel PCA and De-noising in feature spaces
In: Advances in Neural Information Processing Systems 11, ed. by M. S. Kearns and S. A. Solla and D. A. Cohn, pp. 536 – 542, Cambridge, MA, MIT Press.

Mukherjee, S, Tamayo, P, Mesirov, J, Slonim, D, Verri, A, and Poggio, T (1999).
Support Vector Machine Classification of Microarray Data
CBCL.

Neal, R (1999).
Regression and classification using Gaussian process priors (with discussion)
Bayesian Statistics, 6:475-501.

 

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