Recursive reduced least squares support vector regression

Authors:
Yongping Zhao;Jianguo Sun
Affiliations:
Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
Venue:
Pattern Recognition
Year:
2009

Citing 23
Cited 5

The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Making large-scale support vector machine learning practical

Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Reduced Rank Kernel Ridge Regression

Neural Processing Letters
SMO algorithm for least-squares SVM formulations

Neural Computation
SVMTorch: support vector machines for large-scale regression problems

The Journal of Machine Learning Research
Exact simplification of support vector solutions

The Journal of Machine Learning Research
Benchmarking Least Squares Support Vector Machine Classifiers

Machine Learning
Practical selection of SVM parameters and noise estimation for SVM regression

Neural Networks
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods

The Journal of Machine Learning Research
Fast Leave-One-Out Evaluation and Improvement on Inference for LS-SVMs

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression

Pattern Recognition
An overview of statistical learning theory

IEEE Transactions on Neural Networks
Improvements to the SMO algorithm for SVM regression

IEEE Transactions on Neural Networks
Pruning error minimization in least squares support vector machines

IEEE Transactions on Neural Networks
A study on reduced support vector machines

IEEE Transactions on Neural Networks
An improved conjugate gradient scheme to the solution of least squares SVM

IEEE Transactions on Neural Networks
SMO-based pruning methods for sparse least squares support vector machines

IEEE Transactions on Neural Networks
Reduced Support Vector Machines: A Statistical Theory

IEEE Transactions on Neural Networks
Comments on “Pruning Error Minimization in Least Squares Support Vector Machines”

IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine

IEEE Transactions on Neural Networks

Optimized fixed-size kernel models for large data sets

Computational Statistics & Data Analysis
Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive Gaussian particle swarm optimization

Information Sciences: an International Journal
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
Letters: A novel online adaptive kernel method with kernel centers determined by a support vector regression approach

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Combining reduced technique with iterative strategy, we propose a recursive reduced least squares support vector regression. The proposed algorithm chooses the data which make more contribution to target function as support vectors, and it considers all the constraints generated by the whole training set. Thus it acquires less support vectors, the number of which can be arbitrarily predefined, to construct the model with the similar generalization performance. In comparison with other methods, our algorithm also gains excellent parsimoniousness. Numerical experiments on benchmark data sets confirm the validity and feasibility of the presented algorithm. In addition, this algorithm can be extended to classification.