KM $$^{3}$$ SVM: A Efficient Min-Max Modular Support Vector Machine Based on Clustering

Xiyuan Zheng; Xiaonan Fang; Yanyan Tan; Lili Meng; Huaxiang Zhang

doi:10.1007/978-3-030-24271-8_42

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KM $$^{3}$$ SVM: A Efficient Min-Max Modular Support Vector Machine Based on Clustering

Xiyuan Zheng Xiaonan Fang Yanyan Tan Lili Meng Huaxiang Zhang

Year: 2019 Lecture notes in computer science Pages: 467-475 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-24271-8_42

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Keywords:

Cluster analysis Computer science Support vector machine Artificial intelligence Modular design Pattern recognition (psychology) Machine learning Series (stratigraphy) Data mining

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Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Image Retrieval and Classification Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

KM $$^{3}$$ SVM: A Efficient Min-Max Modular Support Vector Machine Based on Clustering

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