SVM uses a hyper-plane to divide the classification space into two parts, ignoring the distributional characteristics and structure of each class. Although THSVM embodies the class distribution, it does not implement SRM and cannot be consistence between the training and testing process.
Maximal margin hyper-sphere SVM for binary pattern classification
dl.acm.org › j.engappai.2022.105615
Jan 1, 2023 · In this paper, we propose a novel maximal margin hyper-sphere support vector machine (MMHS-SVM) for binary pattern classification.
Wang, A non-convex robust small sphere and large margin support vector machine for imbalanced data classification, Neural Comput. Appl. Wen, Maximal-margin ...
This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years, including hybrid ...
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Maximal margin hyper-sphere SVM for binary pattern classification - Free download as PDF File (.pdf), Text File (.txt) or read online for free.
In this paper, we propose a novel maximal margin hyper-sphere support vector machine (MMHS-SVM) for binary pattern classification. Our proposed MMHS-SVM ...
In this paper we present a formulation of the linear SVM that generalizes it to a spherical separation shape, but still maximizing the margin. A linear ...
Feb 15, 2019 · This paper formulates a support vector machine with quantile hyper-spheres (QHSVM) for pattern classification.
Maximal margin hyper-sphere SVM for binary pattern classification - Free download as PDF File (.pdf), Text File (.txt) or read online for free.
Ke, Maximal margin hyper-sphere SVM for binary pattern classification, Engineering Applications of Artificial Intelligence, № 117 https://doi.org/10.1016/j ...