EasySVM: A visual analysis approach for open-box support vector machines

EasySVM: A visual analysis approach for open-box support vector machines

Abstract Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a mod...

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Bibliographic Details
Main Authors: Yuxin Ma, Wei Chen, Xiaohong Ma, Jiayi Xu, Xinxin Huang, Ross Maciejewski, Anthony K. H. Tung
Format: Article
Language:English
Published: SpringerOpen 2017-03-01
Series:Computational Visual Media
Subjects:
support vector machines (SVMs)
rule extraction
visual classification
high-dimensional visualization
visual analysis
Online Access:http://link.springer.com/article/10.1007/s41095-017-0077-5

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http://link.springer.com/article/10.1007/s41095-017-0077-5

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