16149 Kristin Bennett/Erin Bredensteiner: Duality and geometry in SVM classifiers. Internet 2000, 8p. 16150 Kristin Bennett/Colin Campbell: Support vector machines - hype or hallelujah? SIGKDD Explorations 2 (2000), 1-13. Alain Berlinet/Christine Thomas-Agnan: Reproducing kernel Hilbert spaces in probability and statistics. Kluwer 2003, 380p. $125. 16169 Bernhard Boser/Isabelle Guyon/Vladimir Vapnik: A training algorithm for optimal margin classifiers. Internet 1992, 9p. 16158 Christopher Burges: Geometry and invariance in kernel based methods. Internet 1998, 32p. C. Chang/C. Lin: libsvm - a library for support vector machines. Internet 2001. C. Cortes/V. Vapnik: Support vector machines. Machine Learning 20 (1995), 1-25. 16157 David Crisp/Christopher Burges: A geometric interpretation of v-SVM classifiers. Internet 2000, 7p. 16160 Nello Cristianini/John Shawe-Taylor: An introduction to support vector machines and other kernel-based learning methods. Cambridge UP 2000, 190p. $42. 16159 Ayhan Demiriz/Kristin Bennett/Curt Breneman/Mark Embrechts: Support vector machine regression in chemometrics. Internet 2001, 9p. 16230 Theodoros Evgeniou: Learning with kernel machine architectures. PhD thesis MIT 2000, 106p. 16232 Theodoros Evgeniou/Massimiliano Pontil: Statistical learning theory - a primer. Internet ca. 2000, 6p. 16233 Theodoros Evgeniou/Massimiliano Pontil/Tomaso Poggio: Regularization networks and support vector machines. Internet 1999, 53p. 16183 Jeff Fortuna/David Capson: Improved support vector classification using PCA and ICA feature space modification. Pattern Rec. 37 (2004), 1117-1129. 16234 Federico Girosi: An equivalence between sparse approximation and support vector machines. Neural Comp. 10/6 (1998), 1455-1480. 16173 Francisco Gonzalez Castano/Ubaldo Garcia Palomares/Robert Meyer: Projection support vector machine generators. Machine Learning 54 (2004), 33-44. Isabelle Guyon: SVM application survey. Internet 1999. Isabelle Guyon/J. Makhoul/R. Schwartz/Vladimir Vapnik: What size test gives good error estimates? PAMI 20/1 (1998), 52-64. 16197 Trevor Hastie/Robert Tibshirani/Jerome Friedman: The elements of statistical learning. Springer 2001, 520p. $71. 15836 David Meyer: Support vector machines. R News 1/3 (2001), 23-26. The R libsvm library. 17798 Tomaso Poggio/Steven Smale: The mathematics of learning - dealing with data. Notices AMS May 2003, 537-544. 16175 Thomas Walter Rauber: Pattern recognition. A short course. Internet 1997, 42p. 16297 Bernhard Schölkopf/Klaus-Robert Müller/Alexander Smola: Lernen mit Kernen. Informatik Forsch. Entw. 14 (1999), 154-163. 16156 Bernhard Schölkopf/Alexander Smola: Learning with kernels. MIT Press 2002, 620p. $56. Bernhard Schölkopf/Alexander Smola/R. Williamson/P. Bartlett: New support vector algorithms. Neural Computation 12 (2000), 1207-1245. 17795 Alexander Smola/S. Vishwanathan: Hilbert space embeddings in dynamical systems. Internet 2003, 6p. 27532 Ingo Steinwart/Andreas Christmann: Support vector machines. Springer 2008, 610p. 16174 David Tax/Robert Duin: Support vector data description. Machine Learning 54 (2004), 45-66. 16202 Vladimir Vapnik: Statistical learning theory. Wiley 1998, 730p. $115. 16168 Christian Walder/Brian Lovell: Kernel based algebraic curve fitting. Internet ca. 1999, 4p. 16167 Christian Walder/Brian Lovell/Peter Kootsookos: Algebraic curve fitting support vector machines. Internet ca. 2000, 12p. 16164 Sumio Watanabe: Algebraic analysis for non-regular learning machines. Adv. Neural Inf. Proc. Systems 12 (2000), 356-362. 16165 Sumio Watanabe: Algebraic analysis for non-identifiable learning machines. Internet 2000, 36p. 16176 Sumio Watanabe: Algebraic geometrical methods for hierarchical learning machines. Neural Networks 14 (2001), 1049-1060. 16166 Sumio Watanabe: Algebraic information geometry for learning machines with singularities. Internet ca. 2002, 7p.