Publications

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

There are 357 references in this bibliography folder.

Baudat, G and Anouar, F (Washington, DC July 15 - 19, 2001).
Kernel-Based Methods and Function Approximation
In: International Joint Conference on Neural Networks, pp. 1244 - 1249.

T., VG, J.A.K., S, G., L, A., L, B., DM, and J., V (May 2002).
Bayesian Framework for Least Squares Support Vector Machine Classifiers, Gaussian processes and kernel Fisher discriminant analysis
Neural Computation, 14(5):1115-1147.

T., VG, J., S, B., DM, and J., V (Jul. 2001).
Automatic relevance determination for least squares support vector machine classifiers
In: Proc. of the International Joint Conference on neural networks, IJCNN'01, Washington DC, USA, pp. 2416-2421.

T., VG, J., S, G., L, A., L, B., DM, and J., V (Feb. 2002).
Multiclass LS-SVMs : Moderated outputs and coding-decoding schemes
Neural Processing Letters, 15(1):45-48.

Suykens, J (Aug. 2001).
Support Vector Machines : a nonlinear modelling and control perspective
European Journal of Control, Special Issue on fundamental issues in control, 7(2-3):311-327.

T., VG, J., S, Jr., DB, B., DM, and J., V (Aug. 2001).
Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines
In: Proc. of the International Conference on Artificial Neureal Networks (ICANN 2001), Vienna, Austria, pp. 381-386.

T., VG, J., S, B., DM, and J., V (Apr. 2001).
Automatic relevance determination for least squares support vector machine classifiers
In: Proc. of the European Symposium on Artificial Neural Networks (ESANN'2001) , Bruges, Belgium, pp. 13-18.

Ben-Hur, A, Ong, C, Sonnenburg, S, Schölkopf, B, and Rätsch, G (2008).
Support Vector Machines and Kernels for Computational Biology
PLoS Computational Biology, 4.

Hofmann, T, Schölkopf, B, and Smola, AJ (2008).
Kernel Methods in Machine Learning
Annals of Statistics, 36:1171-1220.

Rasmussen, CE and Williams, CK (2006).
Gaussian Processes for Machine Learning

Wenming Zheng, LZ and Zou, C (2005).
Foley-Sammon Optimal Discriminant Vectors Using kernel
IEEE Transactions on Neural Networks, 16(1):1-9.

Burges, C (2004).
Geometric Methods for Feature Extraction and Dimensional Reduction: A Guided Tour
.

Hamers, B (2004).
Kernel Models for Large Scale Applications
PhD thesis.

Monteiro, Ad (2004).
Interest Rate Curve Estimation: a Financial Application for Support Vector Regression
.

Ralaivola, L and d'Alché-Buc, F (2004).
DYnamical Modeling with kernels for Nonlinear Time Series
In: .

Seeger, M (2004).
Gaussian Processes for Machine Learning
International Journal of Neural Systems, 14(2):1–38.

Wenming Zheng, LZ and Zou, C (2004).
A Modified Algorithm for Generalized Discriminant
Neural Computation, 16(6):pp.1283-1297.

Mitra, P, Murthy, CA, and Pal, SK (2003 (to appear)).
A Probabilistic Active Support Vector Learning Algorithm
IEEE Trans. Pattern Analysis and Machine Intelligence.

Baudat, G and Anouar, F (2003).
Feature vector selection and projection using kernels
Neurocomputing, 55(1-2):21-38.

Chen, P, Lin, C, and Schölkopf, B (2003).
A tutorial on $\nu$-Support Vector Machines
..

Chew, H, Lim, C, and Bogner, R (2003).
An Implementation of Training Dual-nu Support Vector Machines
In: Optimization and Control with Applications, ed. by Qi and Teo and Yang, Kluwer.

Christmann, A and Steinwart, I (2003).
On robustness properties of convex risk minimization methods for pattern recognition
.

Fei Sha, LK and Lee, DD (2003).
Multiplicative updates for nonnegative quadratic programming in support vector machines
In: Advances in Neural Information Processing Systems 15, ed. by Suzanna Becker, Sebastian Thrun and Klaus Obermayer, Cambridge, MA, MIT Press.

Fleuret, F and Sahbi, H (2003).
Scale-Invariance of Support Vector Machines Based on the Triangular Kernel
.

J.A.K., S, T., VG, J., V, and B., DM (2003).
A support vector machine formulation to PCA analysis and its Kernel version
IEEE Transactions on Neural Networks, 14(2):447–450.