Schölkopf, Bernhard (ed.); Burges, Christopher J. C. (ed.); Smola, Alexander J. (ed.) Advances in Kernel methods. Support vector learning. (English) Zbl 0935.68084 London: MIT Press. 392 p. (1998). Publisher’s description: The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who’s Who of this exciting new area. Cited in 1 ReviewCited in 30 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence 68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science Keywords:support vector machine Software:MINOS PDFBibTeX XMLCite \textit{B. Schölkopf} (ed.) et al., Advances in Kernel methods. Support vector learning. London: MIT Press (1998; Zbl 0935.68084)