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Machine Learning Code in Wolfram Mathematica exploring the bayesian option, random forest, decision tree, k means, support vector and threshold.
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An Intuitive and simple approach to Support Vector Machines. Estimated y (hyperplane) is f(x), weights are obtained through a gradient descent algorithm and a Gaussian Kernel was used. Commonest distance between variables VI1 and VI2 were obtained with a histogram. The hyperplane doesn't perfectly fit distance/2 because no constraints were applied. But certainly this intuitive approach it's much better than a linear regression and also useful as a classification algorithm, better than a K Nearest Neighbor, given that the perceptron is a "less" powerful KNN.
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Rubens Zimbres, PhD
CV Hazmin Galim
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Support Vector Machines - Wolfram Mathematica
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