Jan 26, 2015 · In this paper, we introduce a post-processing algorithm, which compresses the learned SVM model by reducing and optimizing support vectors.
In this paper, we introduce a post-processing algorithm, which compresses the learned SVM model by reducing and optimizing support vectors.
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A post-processing algorithm is introduced, which compresses the learned SVM model by reducing and optimizing support vectors and demonstrates that it ...
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model ...
Duration: 22:41
Posted: Dec 25, 2021
Posted: Dec 25, 2021
The reduced support vector machine was proposed for the practical objective to overcome the computational burden in generating a nonlinear SVM for the large ...
Feb 1, 2021 · We are compressing high dimensional vectors to 2 dimensions using PCA, so that we can make 2D SVM plots. But it is difficult to compress to ...
The goal of compressing a support vector machine is to replace the original optimization problem by a problem with fewer optimization variables that can be ...
Methods exploring the application of support vector machine learning (SVM) to still image compression are detailed in both the spatial and frequency domains ...
In the case of linear SVM each decision function consists of a single "compressed" support vector. The method returns rho parameter of the decision function, a ...