This paper appears in: Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE
Publication Date: 21-23 May 2001
Volume: 1,
On page(s): 287-294 vol.1
Meeting Date: 05/21/2001 - 05/23/2001
Location: Budapest, Hungary
ISBN: 0-7803-6646-8
References Cited: 40
INSPEC Accession Number: 7028639
DOI: 10.1109/IMTC.2001.928828
Posted online: 2002-08-07 00:24:08.0
Abstract
Neural networks such as multilayer perceptrons and radial basis
function networks have been very successful in a wide range of problems.
In this paper we give a short introduction to some new developments
related to support vector machines (SVM), a new class of kernel based
techniques introduced within statistical learning theory and structural
risk minimization. This new approach lends to solving convex
optimization problems and also the model complexity follows from this
solution. We especially focus on a least squares support vector machine
formulation (LS-SVM) which enables to solve highly nonlinear and noisy
black-box modelling problems, even in very high dimensional input
spaces. While standard SVMs have been basically only applied to static
problems like classification and function estimation, LS-SVM models have
been extended to recurrent models and use in optimal control problems.
Moreover, using weighted least squares and special pruning techniques,
LS-SVMs can be employed for robust nonlinear estimation and sparse
approximation. Applications of (LS)-SVMs to a large variety of
artificial and real-life data sets indicate the huge potential of these
methods
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