What Is Support Vector Machine (SVM)?

Have you ever heard of the SVM? It's a machine learning algorithm that analyzes data for classification and regression analysis. Like your friend who's good at sorting their friends into two categories: those who love them and those who don't. For example, your SVM friend might say: "You are a kind person who should never be in charge of making the rules. " SVMs are used in text categorization, handwriting recognition, and the sciences. They look at data and sort it into one of two categories—supervised learning. The output from an SVM is a map of the data with the margins between the two as far apart as possible—kinda like how your SVM friend says things like, "You're so great! " A support vector machine is a learning algorithm that divides the data into two categories. The task of an SVM algorithm is to determine which class a new data point belongs in. It makes one of the SVM a kind of non-binary linear classifier. The SVM algorithm takes in a set of training data, which contains both ends from the positive group (or "class") and moments from the opposing group (or "not class"). The algorithm then plots each point in this training data on an axis between the two classes based on its feature values (this process is also known as "projection"). A line called the "margin" is plotted along this axis; this line defines where all positive points will lie on one side, and all negative points will lie on the other. Once you have defined your margin, you can train your SVM by determining how far each point is from that line. As long as you do this for every moment in your training data, you end up with an optimal separating hyperplane that will always classify new topics correctly.