Editorial: Kernel Methods: Current Research and Future Directions Nello CristianiniColin CampbellChris Burges Editorial Board Pages: 5 - 9
On a Connection between Kernel PCA and Metric Multidimensional Scaling Christopher K.I. Williams OriginalPaper Pages: 11 - 19
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities Peter Sollich OriginalPaper Pages: 21 - 52
Hierarchical Learning in Polynomial Support Vector Machines Sebastian Risau-GusmanMirta B. Gordon OriginalPaper Pages: 53 - 70
A Probabilistic Framework for SVM Regression and Error Bar Estimation J.B. GaoS.R. GunnM. Brown OriginalPaper Pages: 71 - 89
On the Dual Formulation of Regularized Linear Systems with Convex Risks Tong Zhang OriginalPaper Pages: 91 - 129
Choosing Multiple Parameters for Support Vector Machines Olivier ChapelleVladimir VapnikSayan Mukherjee OriginalPaper Pages: 131 - 159
Training Invariant Support Vector Machines Dennis DecosteBernhard Schölkopf OriginalPaper Pages: 161 - 190
Support Vector Machines for Classification in Nonstandard Situations Yi LinYoonkyung LeeGrace Wahba OriginalPaper Pages: 191 - 202
An Analytic Center Machine Theodore B. TrafalisAlexander M. Malyscheff OriginalPaper Pages: 203 - 223
Linear Programming Boosting via Column Generation Ayhan DemirizKristin P. BennettJohn Shawe-Taylor OriginalPaper Pages: 225 - 254
Large Scale Kernel Regression via Linear Programming O.L. MangasarianDavid R. Musicant OriginalPaper Pages: 255 - 269
Efficient SVM Regression Training with SMO Gary William FlakeSteve Lawrence OriginalPaper Pages: 271 - 290
A Simple Decomposition Method for Support Vector Machines Chih-Wei HsuChih-Jen Lin OriginalPaper Pages: 291 - 314
Feasible Direction Decomposition Algorithms for Training Support Vector Machines Pavel Laskov OriginalPaper Pages: 315 - 349
Convergence of a Generalized SMO Algorithm for SVM Classifier Design S.S. KeerthiE.G. Gilbert OriginalPaper Pages: 351 - 360
Gene Selection for Cancer Classification using Support Vector Machines Isabelle GuyonJason WestonVladimir Vapnik OriginalPaper Pages: 389 - 422
Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? Edda LeopoldJörg Kindermann OriginalPaper Pages: 423 - 444