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Pattern Recognition with Support Vector Machines
. Ed(S): Lee, Seong-Whan; Verri, Alessandro
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Description for Pattern Recognition with Support Vector Machines
Paperback. These are the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002. Editor(s): Lee, Seong-Whan; Verri, Alessandro. Series: Lecture Notes in Computer Science. Num Pages: 440 pages, biography. BIC Classification: UYQP. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 233 x 155 x 22. Weight in Grams: 1350.
With their introduction in 1995, Support Vector Machines (SVMs) marked the beginningofanewerainthelearningfromexamplesparadigm.Rootedinthe Statistical Learning Theory developed by Vladimir Vapnik at AT&T, SVMs quickly gained attention from the pattern recognition community due to a n- beroftheoreticalandcomputationalmerits.Theseinclude,forexample,the simple geometrical interpretation of the margin, uniqueness of the solution, s- tistical robustness of the loss function, modularity of the kernel function, and over?t control through the choice of a single regularization parameter. Like all really good and far reaching ideas, SVMs raised a number of - terestingproblemsforboththeoreticiansandpractitioners.Newapproachesto Statistical Learning Theory are under development and new and more e?cient methods for computing SVM with a ... Read more
With their introduction in 1995, Support Vector Machines (SVMs) marked the beginningofanewerainthelearningfromexamplesparadigm.Rootedinthe Statistical Learning Theory developed by Vladimir Vapnik at AT&T, SVMs quickly gained attention from the pattern recognition community due to a n- beroftheoreticalandcomputationalmerits.Theseinclude,forexample,the simple geometrical interpretation of the margin, uniqueness of the solution, s- tistical robustness of the loss function, modularity of the kernel function, and over?t control through the choice of a single regularization parameter. Like all really good and far reaching ideas, SVMs raised a number of - terestingproblemsforboththeoreticiansandpractitioners.Newapproachesto Statistical Learning Theory are under development and new and more e?cient methods for computing SVM with a ... Read more
Product Details
Format
Paperback
Publication date
2002
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
440
Condition
New
Series
Lecture Notes in Computer Science
Number of Pages
428
Place of Publication
Berlin, Germany
ISBN
9783540440161
SKU
V9783540440161
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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