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2017 | OriginalPaper | Chapter

Parallel Algorithm of Local Support Vector Regression for Large Datasets

Authors : Le-Diem Bui, Minh-Thu Tran-Nguyen, Yong-Gi Kim, Thanh-Nghi Do

Published in: Future Data and Security Engineering

Publisher: Springer International Publishing

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Abstract

We propose the new parallel algorithm of local support vector regression (local SVR), called kSVR for effectively dealing with large datasets. The learning strategy of kSVR performs the regression task with two main steps. The first one is to partition the training data into k clusters, followed which the second one is to learn the SVR model from each cluster to predict the data locally in the parallel way on multi-core computers. The kSVR algorithm is faster than the standard SVR for the non-linear regression of large datasets while maintaining the high correctness in the prediction. The numerical test results on datasets from UCI repository showed that our proposed kSVR is efficient compared to the standard SVR.

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previous chapter IFIN+: A Parallel Incremental Frequent Itemsets Mining in Shared-Memory Environment

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It must be noted that the complexity of the kSVR approach does not include the kmeans clustering used to partition the full dataset. But this step requires insignificant time compared with the quadratic programming solution.

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Title: Parallel Algorithm of Local Support Vector Regression for Large Datasets
Authors: Le-Diem Bui
Minh-Thu Tran-Nguyen
Yong-Gi Kim
Thanh-Nghi Do
Publisher: Springer International Publishing
Book: Future Data and Security Engineering
Print ISBN: 978-3-319-70003-8

Electronic ISBN: 978-3-319-70004-5

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-70004-5_10

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