Incremental learning for <mml:math altimg="si17.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd" xmlns:sa="http://www.elsevier.com/xml/common/struct-aff/dtd"><mml:mi>ν</mml:mi></mml:math>-Support Vector Regression-Reference-Cited by-同舟云学术

Incremental learning for ν-Support Vector Regression

Published:2015-07 Issue: Volume:67 Page:140-150
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Gu Bin,Sheng Victor S.,Wang Zhijie,Ho Derek,Osman Said,Li Shuo

Funder

U.S. National Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference36 articles.

1. Convex optimization;Boyd,2004

2. Support vector machine with adaptive parameters in financial time series forecasting;Cao;IEEE Transactions on Neural Networks,2003

3. Incremental and decremental support vector machine learning;Cauwenberghs,2001

4. Chang, Chih-Chung, & Lin, Chih-Jen (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

5. Training ν-support vector regression: Theory and algorithms;Chang;Neural Computation,2002

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