Cited By
View all- Erkinay Ozdemir MAli ZSubeshan BAsmatulu E(2021)Applying machine learning approach in recyclingJournal of Material Cycles and Waste Management10.1007/s10163-021-01182-yOnline publication date: 17-Feb-2021
Handling Non-linear Relations in Support Vector Machines through Hyperplane Folding
Pages 137 - 141
Abstract
We present a new method, called hyperplane folding, that increases the margin in Support Vector Machines (SVMs). Based on the location of the support vectors, the method splits the dataset into two parts, rotates one part of the dataset and then merges the two parts again. This procedure increases the margin as long as the margin is smaller than half of the shortest distance between any pair of data points from the two different classes. We provide an algorithm for the general case with n-dimensional data points. A small experiment with three folding iterations on 3-dimensional data points with non-linear relations shows that the margin does indeed increase and that the accuracy improves with a larger margin. The method can use any standard SVM implementation plus some basic manipulation of the data points, i.e., splitting, rotating and merging. Hyperplane folding also increases the interpretability of the data.
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Index Terms
- Handling Non-linear Relations in Support Vector Machines through Hyperplane Folding
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Published In
February 2019
563 pages
ISBN:9781450366007
DOI:10.1145/3318299
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- Southwest Jiaotong University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 February 2019
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ICMLC '19
ICMLC '19: 2019 11th International Conference on Machine Learning and Computing
February 22 - 24, 2019
Zhuhai, China
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View all- Erkinay Ozdemir MAli ZSubeshan BAsmatulu E(2021)Applying machine learning approach in recyclingJournal of Material Cycles and Waste Management10.1007/s10163-021-01182-yOnline publication date: 17-Feb-2021
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