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https://www.quora.com/What-are-C-and-gamma-with-regards-to-a-support-vector-machine
C is the cost of misclassification as correctly stated by Dima. A large C gives you low bias and high variance. Low bias because you penalize the cost of missclasification a lot. A small C gives you higher bias and lower variance. Gamma is the par...
https://towardsdatascience.com/support-vector-machine-simply-explained-fee28eba5496
Jan 07, 2019 · Support vector machine with a polynomial kernel can generate a non-linear decision boundary using those polynomial features. Radial Basis Function (RBF) kernel Think of the Radial Basis Function kernel as a transformer/processor to generate new features by measuring the distance between all other dots to a specific dot/dots — centers.Author: Lujing Chen
https://towardsdatascience.com/understanding-support-vector-machine-part-2-kernel-trick-mercers-theorem-e1e6848c6c4d
Dec 19, 2018 · I have used Radial Basis Function kernel to plot figure 2, where mapping from 2D space to 3D space indeed helps us in classification. Apart from this predefined kernels, what conditions determine which functions can be considered as Kernels ?This is given by Mercer’s theorem. First condition is rather trivial i.e. the Kernel function must be symmetric.Author: Saptashwa Bhattacharyya
https://www.learnopencv.com/support-vector-machines-svm/
Jul 11, 2018 · Support Vector Machine (SVM) essentially finds the best line that separates the data in 2D. ... The above expression is called a Gaussian Radial Basis Function or a Radial Basis Function with a Gaussian kernel. We can see the new 3D data is separable by the plane containing the black circle!
https://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html
Support Vector Machine kernel selection can be tricky, and is dataset dependent. ... Overviews » How to Select Support Vector Machine Kernels ( 16:n21 ) How to Select Support Vector Machine Kernels = Previous post. ... and nonlinear kernels such as the Radial Basis Function kernel for non-linear problems.
https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_support_vector_machine.htm
Introduction to SVM. Support vector machines (SVMs) are powerful yet flexible supervised machine learning algorithms which are used both for classification and regression. But generally, they are used in classification problems. In 1960s, SVMs were first introduced but later they got refined in 1990.
https://www.sciencedirect.com/science/article/pii/S221083271830365X
Dec 20, 2018 · Five different models have been developed using supervised learning to detect whether the patient is diabetic or non-diabetic. For this purpose linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-NN, ANN and MDR algorithm are used.Cited by: 5
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