Pair- -SVR: A Novel and Efficient Pairing nu-Support Vector Regression Algorithm

This paper proposes a novel and efficient pairing nu-support vector regression (pair-v-SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical ε-SVR algorithms. In spirit of TSVR, the proposed pair-v-SVR solves two quadratic programmin...

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Published in:IEEE transaction on neural networks and learning systems Vol. 28; no. 11; pp. 2503 - 2515
Main Author: Hao, Pei-Yi
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Complexity theory
Learning systems
Learning theory
Noise heteroscedastic model
Predictions
Quadratic programming
regression estimation
Regression models
Regularization
Risk management
Sparse matrices
Statistical analysis
Support vector machines
support vector machines (SVMs)
support vector regression
Training
Upper bound
Learning systems
Support vector machines
Training
Upper bound
Complexity theory
Risk management
Sparse matrices
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This paper proposes a novel and efficient pairing nu-support vector regression (pair-v-SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical ε-SVR algorithms. In spirit of TSVR, the proposed pair-v-SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical ε-SVR. The significant advantage of our pair-v-SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory. Moreover, pair-v-SVR has additional advantage of using parameter v for controlling the bounds on fractions of SVs and errors. Furthermore, the upper bound and lower bound functions of the regression model estimated by pair-v-SVR capture well the characteristics of data distributions, thus facilitating automatic estimation of the conditional mean and predictive variance simultaneously. This may be useful in many cases, especially when the noise is heteroscedastic and depends strongly on the input values. The experimental results validate the superiority of our pair-v-SVR in both training/prediction speed and generalization ability.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2598182