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Aug 13, 2015 · To solve this problem, the graph regularization term with discrimination information is introduced into the objective function of SVM model.
To solve this problem, the graph regularization term with discrimination information is introduced into the objective function of SVM model. Experimental ...
Traditional SVM classification model constructs linear discriminant function by maximizing the margin between two classes, and the weight vector of the ...
In this paper a variant of the binary Support Vector Machine classifier that exploits intrinsic and penalty graphs in its optimization problem is proposed. We ...
May 20, 2016 · This paper proposes Fisher regularized support vector machine (FisherSVM). •. FisherSVM is a graph-based supervised learning method.
• C is a regularization parameter: — small C allows constraints to be easily ignored → large margin. — large C makes constraints hard to ignore → narrow ...
Jan 12, 2019 · In this article we cover techniques to visualise learned SVM models and their performance on real world data.
The gamma parameter defines how far the influence of a single training example reaches, with low values meaning 'far' and high values meaning 'close'.
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.
Jul 22, 2020 · A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph ...
Missing: discriminant | Show results with:discriminant