1999 AdvancesInKernelMethods

• (Schölkopf et al., 1999) ⇒ Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola. (1999). “Advances in Kernel Methods - Support Vector Learning.” In: MIT Press. ISBN:0-262-19416-3

Subject Headings: Kernel-based Algorithm.

Notes

• It contains (Joachims, 1999) ⇒ Thorsten Joachims. (1999). “Making Large-Scale SVM Learning Practical.” In: (Schölkopf et al., 1999).

Cited By

• ~1060 http://scholar.google.com/scholar?q=%22Advances+in+Kernel+Methods+-+Support+Vector+Learning%22+1999

Quotes

Book Overview

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 AdvancesInKernelMethods	Bernhard Schölkopf Alexander J. Smola Christopher J. C. Burges			Advances in Kernel Methods - Support Vector Learning			http://books.google.com/books?id= NYamXKkNM8C