Inproceedings (boser92svmtraining)
Boser, B. E.; Guyon, I. M. & Vapnik, V. N.
Haussler, D. (ed.)
A Training Algorithm for Optimal Margin Classifiers
ACM Press, New York, NY, USA, 1992, 144-152

Inproceedings (cai04hierarchical)
Cai, L. & Hofmann, T.
Hierarchical Document Categorization with Support Vector Machines
ACM Press, New York, NY, USA, 2004, 78-87

Inproceedings (joachims00estimating)
Joachims, T.
Estimating the Generalization Performance of an SVM Efficiently
Morgan-Kaufman Publishers, San Francisco, CA, USA, 2000, 431-438

Inproceedings (joachims99svmlight)
Joachims, T.
Schölkopf, B.; Burges, C. & Smola, A. (ed.)
Making Large-Scale SVM Learning Practical
MIT Press, Cambridge, MA, USA, 1999

Article (kimeldorf71representer)
Kimeldorf, G. S. & Wahba, G.
Some Results on Tchebycheffian Spline Functions
Journal of Mathematical Analysis and Applications, 1971, 33, 82-95

Inproceedings (mierswa06evosvm)
Mierswa, I.
Cattolico, M. (ed.)
Evolutionary Learning with Kernels: A Generic Solution for Large Margin Problems
ACM Press, New York, NY, USA, 2006, 1553-1560

Inproceedings (morik99combining)
Morik, K.; Brockhausen, P. & Joachims, T.
Bratko, I. & Dzeroski, S. (ed.)
Combining Statistical Learning with a Knowledge-Based Approach - a Case Study in Intensive Care Monitoring
Morgan-Kaufman Publishers, San Francisco, CA, USA, 1999, 268-277

Inproceedings (tsochantaridis04structuredsvm)
Tsochantaridis, I.; Hofmann, T.; Joachims, T. & Altun, Y.
Brodley, C. E. (ed.)
Support Vector Machine Learning for Interdependent and Structured Output Spaces
ACM Press, New York, NY, USA, 2004

Inproceedings (vapnik96svm)
Vapnik, V.; Golowich, S. E. & Smola, A. J.
Mozer, M.; Jordan, M. I. & Petsche, T. (ed.)
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing
MIT Press, Cambridge, MA, USA, 1997, 281-287

Book (vapnik95naturestatlearn)
Vapnik, V. N.
The Nature of Statistical Learning Theory
Springer New York Inc., 1995