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This post categorized under Vector and posted on September 4th, 2018.

This article explains support vector machine a machine learning algorithm and its uses in classification and regression. Its a supervised learning algorithmIf you have used machine learning to perform classification you might have heard about Support Vector Machines (SVM). Introduced a little more than 50 years ago they have evolved over time and have also been adapted to various other problems like regression outlier analysis and ranking.A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words given labeled training data ( supervised learning ) the algorithm outputs an optimal hyperplane which categorizes new examples.

Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. In this section we will develop the intuition behind support vector machines and their use in classification problems. As an example of this consider theSupport Vector Machine (SVM) essentially finds the best line that separates the data in 2D. This line is called the Decision Boundary . If we had 1D data we would separate the Support Vector Machine Example Separating two point clouds is easy with a linear line but what if they cannot be separated by a linear line In that case we can use a kernel a kernel is a function that a domain-expert provides to a machine learning algorithm (a kernel is not limited to an svm).

A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such this is what we will focus on in this post.

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