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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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 28; číslo 11; s. 2503 - 2515
Hlavní autor:	Hao, Pei-Yi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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