Jumat, 22 Juli 2011

[Z431.Ebook] Ebook Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle

Ebook Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle

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Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle

Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle



Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle

Ebook Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle

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Least Squares Support Vector Machines, by Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.

  • Sales Rank: #3544805 in Books
  • Brand: Brand: World Scientific Publishing Company
  • Published on: 2002-11-14
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.48" h x .84" w x 6.52" l, 1.23 pounds
  • Binding: Hardcover
  • 308 pages
Features
  • Used Book in Good Condition

Most helpful customer reviews

0 of 0 people found the following review helpful.
Almost what I needed ...
By Peter F.
I am using LS-SVMlab software toolbox and this book is helpful to understand the background. This book is NOT the tutorial I am searching for. Unfortunately, neither is the User's Guide. I'm looking for a detailed tutorial on NARX time series implementation. Still, this book is helpful.

See all 1 customer reviews...

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