1. Editorial: Kernel Methods: Current Research and Future Directions

   • Nello Cristianini
   • Colin Campbell
   • Chris Burges

   Editorial Board Pages: 5 - 9

2. On a Connection between Kernel PCA and Metric Multidimensional Scaling

   • Christopher K.I. Williams

   OriginalPaper Pages: 11 - 19

3. Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities

   • Peter Sollich

   OriginalPaper Pages: 21 - 52

4. Hierarchical Learning in Polynomial Support Vector Machines

   • Sebastian Risau-Gusman
   • Mirta B. Gordon

   OriginalPaper Pages: 53 - 70

5. A Probabilistic Framework for SVM Regression and Error Bar Estimation

   • J.B. Gao
   • S.R. Gunn
   • M. Brown

   OriginalPaper Pages: 71 - 89

6. On the Dual Formulation of Regularized Linear Systems with Convex Risks

   • Tong Zhang

   OriginalPaper Pages: 91 - 129

7. Choosing Multiple Parameters for Support Vector Machines

   • Olivier Chapelle
   • Vladimir Vapnik
   • Sayan Mukherjee

   OriginalPaper Pages: 131 - 159

8. Training Invariant Support Vector Machines

   • Dennis Decoste
   • Bernhard Schölkopf

   OriginalPaper Pages: 161 - 190

9. Support Vector Machines for Classification in Nonstandard Situations

   • Yi Lin
   • Yoonkyung Lee
   • Grace Wahba

   OriginalPaper Pages: 191 - 202

10. An Analytic Center Machine

    • Theodore B. Trafalis
    • Alexander M. Malyscheff

    OriginalPaper Pages: 203 - 223

11. Linear Programming Boosting via Column Generation

    • Ayhan Demiriz
    • Kristin P. Bennett
    • John Shawe-Taylor

    OriginalPaper Pages: 225 - 254

12. Large Scale Kernel Regression via Linear Programming

    • O.L. Mangasarian
    • David R. Musicant

    OriginalPaper Pages: 255 - 269

13. Efficient SVM Regression Training with SMO

    • Gary William Flake
    • Steve Lawrence

    OriginalPaper Pages: 271 - 290

14. A Simple Decomposition Method for Support Vector Machines

    • Chih-Wei Hsu
    • Chih-Jen Lin

    OriginalPaper Pages: 291 - 314

15. Feasible Direction Decomposition Algorithms for Training Support Vector Machines

    • Pavel Laskov

    OriginalPaper Pages: 315 - 349

16. Convergence of a Generalized SMO Algorithm for SVM Classifier Design

    • S.S. Keerthi
    • E.G. Gilbert

    OriginalPaper Pages: 351 - 360

17. The Relaxed Online Maximum Margin Algorithm

    • Yi Li
    • Philip M. Long

    OriginalPaper Pages: 361 - 387

18. Gene Selection for Cancer Classification using Support Vector Machines

    • Isabelle Guyon
    • Jason Weston
    • Vladimir Vapnik

    OriginalPaper Pages: 389 - 422

19. Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?

    • Edda Leopold
    • Jörg Kindermann

    OriginalPaper Pages: 423 - 444