Publications

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 (2001).
A Comparison on Methods for Multi-class Support Vector Machines
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

Hua, S and Sun, Z (2001).
A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach
Journal of Molecular Biology:(in press).

Jianhua XU, XZ (2001).
Kernel Neuron and Its Training Algorithm
In: 8th International conference on neural information, vol. 2, pp. 861-866.

Ke, H and Zhang, X (2001).
Editing Support Vector Machines
Proceedings of IJCNN'01, 2:1464-1467.

Kecman, V (2001).
Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models
MIT Press.

Laskov, P (2001).
Feasible Direction Decomposition Algorithms for Training Support Vector Machines
Machine Learning.

Lee, Y, Lin, Y, and Wahba, G (2001).
Multicategory Support Vector Machines
Department of Statistics, University of Wisconsin, Madison WI.

Liao, S, Lin, H, and Lin, C (2001).
A note on the decomposition methods for support vector regression
NTU.

Lin, C (2001).
Stopping Criteria of Decomposition Methods for Support Vector Machines: a Theoretical Justification
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

Lu, J, Plataniotis, K, and Venetsanopoulos, A (2001).
``Face Recognition Using Feature Optimization and $\nu$-Support Vector Learning''
In: Proceedings of the IEEE International Workshop on Neural Networks for Signal Processing, pp. 373-382, Falmouth, MA., USA.

M\"uller, K, Mika, S, Rätsch, G, and Tsuda, K (2001).
An Introduction to Kernel-Based Learning Algorithms
IEEE Transactions on Neural Networks, 12(2):181–201.

Mangasarian, OL and Musicant, DR (2001).
Lagrangian Support Vector Machines
Journal of Machine Learning Research, 1:161–177.

Müller, K, Mika, S, Rätsch, G, Tsuda, K, and Schölkopf, B (2001).
An Introduction to Kernel-based Learning Algorithms
IEEE Neural Networks, 12(2):181–201.

Rosipal, R and Trejo, L (2001).
Kernel Partial Least Squares Regression in RKHS
CIS Department, University of Paisley, UK.

Ruiz, A and López-de-Teruel, P (2001).
Nonlinear Kernel-Based Statistical Pattern Analysis
IEEE Transactions on Neural Networks, 12(1):16-32.

Rätsch, G, Demiriz, A, and Bennett, KP (2001).
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning Journal.

Schölkopf, B (2001).
SVM and Kernel Methods
Miscellaneous publication.

Smola, A and Bartlett, P (2001).
Sparse Greedy Gaussian Process Regression
In: Advances in Neural Information Processing Systems 13.

Smola, AJ, Mika, S, Schölkopf, B, and Williamson, RC (2001).
Regularized Principal Manifolds
Journal of Machine Learning Research.

Steinwart, I (2001).
On the influence of the kernel on the generalization ability of support vector machines
Jenaer Schriften zur Mathematik und Informatik der FSU Jena, Germany.

Steinwart, I (2001).
On the generalization ability of support vector machines
Technical Report 07-01 FSU Jena.

Suykens, J, Vandewalle, J, and Moor, BD (2001).
Optimal Control by Least Squares Support Vector Machines
Neural Networks, 14(1):23-35.

T., VG, J., S, D., B, A., L, G., L, B., V, B., DM, and J., V (2001).
Financial Time Series Prediction using Least Squares Support Vector Machines within the Evidence Framework
IEEE Transactions on Neural Networks, Special Issue on Neural Networks in Financial Engineering, 12(4):809-821.

Tresp, V (2001).
Mixtures of Gaussian Processes
In: Advances in Neural Information Processing Systems, vol. 13.

Tresp, V (2001).
Scaling Kernel-Based Systems to Large Data Sets
Data Mining and Knowledge Discovery, 5(3):197-211.