Support vector machines, presented for the problem of identifying two groups of points on the plane

Luong Thai Hien * , & Dinh Thi Tam

* Correspondence: Luong Thai Hien (email: HienLT@vhu.edu.vn)

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Received: 17 Mar 2017
Revised: 06 Jun 2017
Accepted: 06 Jun 2017
Published: 10 Jul 2017
DOI: 10.58810/vhujs.5.2.2017.5211
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How to Cite
Luong, H. T., & Dinh, T. T. (2017). Support vector machines, presented for the problem of identifying two groups of points on the plane. Van Hien University Journal of Science 5(2), 106-114.
Issue
Volume 5 - Issue 2 (2017)
Section
Engineering

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Abstract

SVM (Support Vector Machine) is a concept in statistics and computer science for a set of supervised learning methods related to each other for classification and regression analysis. SVM is a binary classification algorithm, Support vector machine (SVM) to build a hyperplane to classify the data set into two separate classes. A hyperplane is a function similar to the line equation, y = ax + b. In fact, if we need to classify a dataset with only two features, the hyperplane is now a straight line. In terms of ideas, SVM uses tricks to map the original dataset to more dimensional spaces. Once mapped to a multidimensional space, SVM will review and select the most suitable superlattice to classify that data set.
Keywords: binary extraction, two-dimensional space, data classification, data clustering, data stratification, identification, SVM (Support Vector Machine).

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References

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