Least Squares Support Vector Machine Classifiers
Neural Processing LettersPublished 1 June 1999Open access
Johan A. K. Suykens, Joos Vandewalle
Citations9,375
SJR quartileQ2
SJR score0.67
SNIP0.92
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Abstract
In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.
Keywords
Computer Science
The Nature of Statistical Learning Theory
39,279 Citations1995Vladimir Vapnik
Neural Networks: A Comprehensive Foundation
29,816 Citations1998Simon Haykin
Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
TechnometricsStatistical Learning Theory
26,913 Citations1999Yuhai Wu, Vladimir Vapnik
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Choice Reviews OnlineNeural networks for pattern recognition
18,687 Citations1994
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Neural Networks for Pattern Recognition
12,198 Citations1995Chris Bishop
The Nature of Statistical Learning Theory
10,566 Citations2000Vladimir Vapnik
Practical Methods of Optimization
6,403 Citations2000R. Ian Fletcher
IEEE Transactions on Signal ProcessingComparing support vector machines with Gaussian kernels to radial basis function classifiers
1,381 Citations1997Bernhard Schölkopf, Kah-Kay Sung +5 more
The results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system, and the SV approach is thus not only theoretically well-founded but also superior in a practical application.
Learning from Data: Concepts, Theory, and Methods
1,293 Citations1998Vladimir Cherkassky, Filip Mulier
ePrints Soton (University of Southampton)Ridge Regression Learning Algorithm in Dual Variables
802 Citations1998Craig Saunders, Alex Gammerman +1 more
A regression estimation algorithm which is a combination of the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinitenode splines and the use of kernel functions, as used in Support Vector methods is introduced.
The Support Vector Method of Function Estimation
614 Citations1998Vladimir Vapnik
For the Support Vector method both the quality of solution and the complexity of the solution does not depend directly on the dimensionality of an input space, and on the basis of this technique one can obtain a good estimate using a given number of high-dimensional data.
IEEE Transactions on Neural NetworksCircular backpropagation networks for classification
137 Citations1997Sandro Ridella, Stefano Rovetta +1 more
The proposed model unifies the two main representation paradigms found in the class of mapping networks for classification, namely, the surface-based and the prototype-based schemes, while retaining the advantage of being trainable by backpropagation.