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Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study
(ELSEVIER SCIENCE BV, 2011)
Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and ...
Learning kernels for support vector machines with polynomial powers of sigmoid
(Ieee, 2014-01-01)
In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into ...
Scalable and interpretable kernel methods based on random Fourier features
(Universidade Federal de São CarlosUFSCarPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsCâmpus São Carlos, 2023-03-29)
Kernel methods are a class of statistical machine learning models based on positive semidefinite kernels, which serve as a measure of similarity between data features. Examples of kernel methods include kernel ridge ...
A kernel-based optimum-path forest classifier
(2018-01-01)
The modeling of real-world problems as graphs along with the problem of non-linear distributions comes up with the idea of applying kernel functions in feature spaces. Roughly speaking, the idea is to seek for well-behaved ...
Inducing Contextual Classifications with Kernel Functions into Support Vector Machines
(2018-06-01)
Kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine ...