Weighted solution path algorithm of support vector regression based on heuristic weight-setting optimization

Authors:
Wentao Mao;Guirong Yan;Longlei Dong
Affiliations:
The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China
Venue:
Neurocomputing
Year:
2009

Citing 19
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Choosing Multiple Parameters for Support Vector Machines

Machine Learning
Predicting Time Series with Support Vector Machines

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Practical selection of SVM parameters and noise estimation for SVM regression

Neural Networks
The Entire Regularization Path for the Support Vector Machine

The Journal of Machine Learning Research
New Support Vector Algorithms

Neural Computation
Two-dimensional solution path for support vector regression

ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Computation and Model Selection for the Support Vector Regression

Neural Computation
Local prediction of non-linear time series using support vector regression

Pattern Recognition
A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression

Neurocomputing
A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

Neurocomputing
Particle Swarm Model Selection

The Journal of Machine Learning Research
Evolutionary tuning of multiple SVM parameters

Neurocomputing
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
Fuzzy support vector machines

IEEE Transactions on Neural Networks
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

IEEE Transactions on Neural Networks
A New Solution Path Algorithm in Support Vector Regression

IEEE Transactions on Neural Networks

Electrical evoked potentials prediction model in visual prostheses based on support vector regression with multiple weights

Applied Soft Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the conventional solution path algorithm of support vector regression, the @e-insensitive error of every training sample is equally penalized, which means every sample affects the generalization ability equally. However, in some cases, e.g. time series prediction or noisy function regression, the @e-insensitive error of the sample which could provide more important information should be penalized more heavily. Therefore, the weighted solution path algorithm of support vector regression is proposed in this paper. Error penalty parameter of each training sample is weighted differently, and the whole solution path is modified correspondingly. More importantly, by choosing Arc Tangent function as the prototype to generate weights with various characteristics, a heuristic weight-setting optimization algorithm is proposed to compute the optimal weights using particle swarm optimization (PSO). This method is applicable to different applications. Experiments on time series prediction and noisy function regression are conducted, demonstrating comparable results of the proposed weighted solution path algorithm and encouraging performance of the heuristic weight-setting optimization.