Programming, AI, Machine Learning & Software Development Tutorials
Introduction Another important improvement to the K-Means algorithm was proposed in a 2003 paper by Charles Elkan. It considerably accelerates the algorithm by avoiding many unnecessary distance calculations: this is achieved by exploiting the triangle inequality (i.e., the straight line is always the shortest) and by keeping track of lower and upper bounds for distances … Read more
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
Introduction In Previous blog we talked about Polynomial Kernel. In this blog we will talk about Gaussian RBF Kernel. Just like the polynomial features method, the similarity features method can be useful with any Machine Learning algorithm, but it may be computationally expensive to compute all the additional features, especially on large training sets. However, … Read more
In Previous blog we talked about Non Linear SVM Classifications. In this blog we will talk about Polynomial Kernel. Introduction Adding polynomial features is simple to implement and can work great with all sorts of Machine Learning algorithms (not just SVMs), but at a low polynomial degree it cannot deal with very complex datasets, and … 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
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