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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIRCC Publishing Corporation
2016
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| 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 |
| 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. |
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