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This paper presents an error analysis for classification algorithms generated by regularization schemes with polynomial kernels. Explicit convergence rates.
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This example illustrates the use of PolynomialCountSketch to efficiently generate polynomial kernel feature-space approximations. This is used to train linear ...
This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector ...
This paper provides an error analysis for the support vector machine (SVM) soft margin classifier with polynomial kernels. The learning rate is estimated by the ...
In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, ...
The trials compare four Kernel functions of SVM, consisting of Dot function, Radial Basis Function (RBF), Sigmoid function, and Polynomial function to evaluate ...
Jan 1, 2016 · In this note, we investigate SVMs classifiers with the polynomial kernels, probably one of the most popular kernels used in SVMs and other ...
Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning. Research, 11:1471–1490, 2010. C. Cortes and V. Vapnik ...
Feb 23, 2024 · The polynomial kernel works well with low-dimensional, dense data, where the SVM algorithm's accuracy and performance may be enhanced by include ...
Dec 26, 2012 · So on the one hand, we have kernelized SVMs, which work quite well on complicated that that is not linearly separable, but doesn't scale well to ...