SMO-based pruning methods for sparse least squares support vector machines

Authors:
Xiangyan Zeng;Xue-wen Chen
Affiliations:
Dept. of Electr. & Comput. Eng., California State Univ., Northridge, CA, USA;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
Cited 13

Nonlinear Systems Modeling Using LS-SVM with SMO-Based Pruning Methods

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
An Adaptive Internal Model Control Based on LS-SVM

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Recursive reduced least squares support vector regression

Pattern Recognition
A normal least squares support vector machine (NLS-SVM) and its learning algorithm

Neurocomputing
An effective method of pruning support vector machine classifiers

IEEE Transactions on Neural Networks
Local weighted LS-SVM online modeling and the application in continuous processes

AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Letters: Using Sequential Unconstrained Minimization Techniques to simplify SVM solvers

Neurocomputing
Improved conjugate gradient implementation for least squares support vector machines

Pattern Recognition Letters
Online independent reduced least squares support vector regression

Information Sciences: an International Journal
Hierarchical modified regularized least squares fuzzy support vector regression through multiscale approach

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Letters: A novel online adaptive kernel method with kernel centers determined by a support vector regression approach

Neurocomputing
Efficient sparse least squares support vector machines for pattern classification

Computers & Mathematics with Applications
Fast sparse approximation of extreme learning machine

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Solutions of least squares support vector machines (LS-SVMs) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS-SVM. In this paper, we propose a new pruning algorithm for sparse LS-SVMs: the sequential minimal optimization (SMO) method is introduced into pruning process; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments.