Publications

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

There are 357 references in this bibliography folder.

K. Fukumizu, FB and Jordan, M (2003).
Dimensionality reduction for supervised learning with Reproducing Kernel Hilbert Spaces
.

Kuss, M and Graepel, T (2003).
The Geometry Of Kernel Canonical Correlation Analysis
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

Lee, Y, Wahba, G, and Ackerman, S (2003).
Classification of Satellite Radiance Data by Multicategory Support Vector Machines
Department of Statistics, University of Wisconsin, Madison WI.

Lin, H and Lin, C (2003).
A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

Lu, J, Plataniotis, K, and Venetsanopoulos, A (2003).
``Face Recognition Using Kernel Direct Discriminant Analysis Algorithms''
IEEE Transactions on Neural Networks, 14(1).

Steinwart, I (2003).
Sparseness of Support Vector Machines
.

Vazquez, E and Walter, E (2003).
Multi-output support vector regression
In: .

Bahlmann, C, Haasdonk, B, and Burkhardt, H (2002).
On-line Handwriting Recognition with Support Vector Machines—A Kernel Approach
In: Proc. of the 8th IWFHR, pp. 49–54.

Chang, M, Lin, C, and Weng, RC (2002).
Analysis of switching dynamics with competing support
In: Proceedings of IJCNN.

Chapelle, O, Vapnik, V, Bousquet, O, and Mukherjee, S (2002).
Choosing multiple parameters for Support Vector Machines
Machine Learning.

D. Zhou, BX and Dai, R (2002).
Global Geometry of SVM Classifiers
.

G. Camps-Valls, ES (2002).
Cyclosporine Concentration Prediction using Clustering and Support Vector Regression
IEE Electronics Letters, 38:568-570.

Guyon, I, Weston, J, Barnhill, S, and Vapnik, V (2002).
Gene selection for cancer classification using support vector machines
Machine Learning, 46:389–422.

Haasdonk, B and Keysers, D (2002).
Tangent Distance Kernels for Support Vector Machines
In: , vol. 2, pp. 864-868.

Hamers, B, Suykens, J, and Moor, BD (2002).
Compactly supported RBF kernels for sparsifying the Gram Matrix in LS-SVM regression models
In: , pp. 720-726.

Isozaki, H and Kazawa, H (2002).
Efficient Support Vector Classifiers for Named Entity Recognition
In: , pp. 390–396.

J.A.K., S, T., VG, J., DB, B., DM, and J., V (2002).
Least Squares Support Vector Machines
World Scientific Publishing Co., Pte, Ltd., Singapore. (ISBN: 981-238-151-1).

Kim, G and Kim, M (2002).
Pattern Recognition of Protein 2D Gel Image and its Application for Diagnosis of a Disease
.

Lee, Y, Lin, Y, and Wahba, G (2002).
Multicategory support vector machines, theory, and application to the classification of microarray data and satellite radiance data
Department of Statistics, University of Wisconsin, Madison WI.

Lewis, DD (2002).
Applying Support Vector Machines to the TREC-2001 Batch Filtering and Routing Tasks
In: Text REtrieval Conference (TREC 2001), ed. by E. M. Voorhees and D. K. Harman, Gaithersburg, MD 20899-0001, National Institute of Standards and Technology.

P., N and J., R (2002).
On the Generalization of Kernel Machines
Pattern Recognition with Support Vector Machines, Springer, 2388:24–39.

R\"atsch, G, Mika, S, Sch\"olkopf, B, and M\"uller, K (2002).
Constructing Boosting Algorithms from SVMs: an Application to One-Class Classification
IEEE PAMI.

Rätsch, G, Mika, S, and Warmuth, M (2002).
On the Convergence of Leveraging
In: Advances in Neural information processings systems, ed. by T.G. Dietterich and S. Becker and Z. Ghahramani, vol. 14.

Sch\"olkopf, B and Smola, AJ (2002).
Learning with Kernels
MIT Press.

Siwei Lyu, HF (2002).
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
In: .