A review on optimization of least squares support vector machine for time series forecasting

Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solut...

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Bibliographic Details
Main Authors: Yusof, Yuhanis, Mustaffa, Zuriani
Format: Article
Language:English
Published: AIRCC Publishing Corporation 2016
Subjects:
Online Access:http://repo.uum.edu.my/18308/1/IJAIA%207%202%202016%2035-49.pdf
http://repo.uum.edu.my/18308/
http://doi.org/10.5121/ijaia.2016.7203
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Institution: Universiti Utara Malaysia
Language: English
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Summary:Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation.