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Introduction As we mentioned earlier, the SVM algorithm is quite versatile: not only does it support linear and nonlinear classification, but it also supports linear and nonlinear regression. The trick is to reverse the objective: instead of trying to fit the largest possible street between two classes while limiting margin violations, SVM Regression tries to … Read more
In previous blog, We read about Soft margin Classification. In this blog we will talk about Non Linear SVM Classification. Introduction Although linear SVM classifiers are efficient and work surprisingly well in many cases, many datasets are not even close to being linearly separable. One approach to handling nonlinear datasets is to add more features, … Read more
In previous blog, We read about Linear SVM. In this blog we will talk about soft margin classification. If we strictly impose that all instances be off the street and on the right side, this is called hard margin classification. There are two main issues with hard margin classification. First, it only works if the … Read more
A Support Vector Machine (SVM) is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification, regression, and even outlier detection. It is one of the most popular models in Machine Learning, and anyone interested in Machine Learning should have it in their toolbox. SVMs are particularly well suited for … Read more