An efficient online learning approach for support vector regression

Jie Liu; Valeria Vitelli; Redouane Seraoui; Enrico Zio

doi:10.1142/9789814619998_0032

An efficient online learning approach for support vector regression

Jie Liu
, Valeria Vitelli
, Redouane Seraoui
, Enrico Zio

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, an efficient online learning approach is proposed for Support Vector Regression (SVR) by combining Feature Vector Selection (FVS) and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can “adiabatically” add a new Feature Vector (FV) in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training & learning and offline training & online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm.

Original language	English
Title of host publication	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014
Editors	Ronei Marcos de Moraes, Etienne E. Kerre, Liliane dos Santos Machado, Jie Lu
Publisher	World Scientific Publishing Co. Pte Ltd
Pages	182-187
Number of pages	6
ISBN (Electronic)	9789814619967
DOIs	https://doi.org/10.1142/9789814619998_0032
State	Published - 2014
Externally published	Yes
Event	Decision Making and Soft Computing - 11th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2014 - Joao Pessoa, Paraiba, Brazil Duration: 17 Aug 2014 → 20 Aug 2014

Publication series

Name	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014

Conference

Conference	Decision Making and Soft Computing - 11th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2014
Country/Territory	Brazil
City	Joao Pessoa, Paraiba
Period	17/08/14 → 20/08/14

Access to Document

10.1142/9789814619998_0032

Cite this

Liu, J., Vitelli, V., Seraoui, R., & Zio, E. (2014). An efficient online learning approach for support vector regression. In R. M. de Moraes, E. E. Kerre, L. dos Santos Machado, & J. Lu (Eds.), Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014 (pp. 182-187). (Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789814619998_0032

Liu, Jie ; Vitelli, Valeria ; Seraoui, Redouane et al. / An efficient online learning approach for support vector regression. Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014. editor / Ronei Marcos de Moraes ; Etienne E. Kerre ; Liliane dos Santos Machado ; Jie Lu. World Scientific Publishing Co. Pte Ltd, 2014. pp. 182-187 (Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014).

@inproceedings{763d5c9f119147a786dd8d1683b1e4e1,

title = "An efficient online learning approach for support vector regression",

abstract = "In this paper, an efficient online learning approach is proposed for Support Vector Regression (SVR) by combining Feature Vector Selection (FVS) and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can “adiabatically” add a new Feature Vector (FV) in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training \& learning and offline training \& online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm.",

author = "Jie Liu and Valeria Vitelli and Redouane Seraoui and Enrico Zio",

note = "Publisher Copyright: {\textcopyright} 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.; Decision Making and Soft Computing - 11th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2014 ; Conference date: 17-08-2014 Through 20-08-2014",

year = "2014",

doi = "10.1142/9789814619998\_0032",

language = "英语",

series = "Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014",

publisher = "World Scientific Publishing Co. Pte Ltd",

pages = "182--187",

editor = "\{de Moraes\}, \{Ronei Marcos\} and Kerre, \{Etienne E.\} and \{dos Santos Machado\}, Liliane and Jie Lu",

booktitle = "Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014",

address = "新加坡",

}

Liu, J, Vitelli, V, Seraoui, R & Zio, E 2014, An efficient online learning approach for support vector regression. in RM de Moraes, EE Kerre, L dos Santos Machado & J Lu (eds), Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014. Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014, World Scientific Publishing Co. Pte Ltd, pp. 182-187, Decision Making and Soft Computing - 11th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2014, Joao Pessoa, Paraiba, Brazil, 17/08/14. https://doi.org/10.1142/9789814619998_0032

An efficient online learning approach for support vector regression. / Liu, Jie; Vitelli, Valeria; Seraoui, Redouane et al.
Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014. ed. / Ronei Marcos de Moraes; Etienne E. Kerre; Liliane dos Santos Machado; Jie Lu. World Scientific Publishing Co. Pte Ltd, 2014. p. 182-187 (Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Seraoui, Redouane

AU - Zio, Enrico

PY - 2014

Y1 - 2014

N2 - In this paper, an efficient online learning approach is proposed for Support Vector Regression (SVR) by combining Feature Vector Selection (FVS) and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can “adiabatically” add a new Feature Vector (FV) in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training & learning and offline training & online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm.

AB - In this paper, an efficient online learning approach is proposed for Support Vector Regression (SVR) by combining Feature Vector Selection (FVS) and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can “adiabatically” add a new Feature Vector (FV) in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training & learning and offline training & online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm.

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U2 - 10.1142/9789814619998_0032

DO - 10.1142/9789814619998_0032

M3 - 会议稿件

AN - SCOPUS:85004032224

T3 - Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014

SP - 182

EP - 187

BT - Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014

A2 - de Moraes, Ronei Marcos

A2 - Kerre, Etienne E.

A2 - dos Santos Machado, Liliane

A2 - Lu, Jie

PB - World Scientific Publishing Co. Pte Ltd

T2 - Decision Making and Soft Computing - 11th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2014

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Liu J, Vitelli V, Seraoui R, Zio E. An efficient online learning approach for support vector regression. In de Moraes RM, Kerre EE, dos Santos Machado L, Lu J, editors, Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014. World Scientific Publishing Co. Pte Ltd. 2014. p. 182-187. (Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014). doi: 10.1142/9789814619998_0032

An efficient online learning approach for support vector regression

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