# Support Vector Machine SVM Explained

This post categorized under Vector and posted on April 7th, 2019.

A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both clgraphicification and regression purposes. SVMs are more commonly used in clgraphicification problems and as such this is what we will focus on in this post.Support Vector Machines explained well By Iddo on February 5th 2014 . Found this on Reddit rmachinelearning (In related news theres a machine learning subreddit.Photo by Malte Schmidt on Unsplash. I was always kind of running away from the support vector machine chapter on my ML books. It is just intimidating you know the name Support Vector Machine.

Well a Support Vector Machine might SVMs are a way computers use math to separate things like pictures of dogs and cats from each other. Just like you SVMs learn to separate things by learning from lots of examples. If an SVM is trained well it should be able to correctly clgraphicify examples it The goal of a support vector machine is to find the optimal separating hyperplane which maximizes the margin of the training data. The first thing we can see from this definition is that a SVM In other words support vector machines calculate a maximum-margin boundary that leads to a graphicgeneous pargraphicion of all data points. This clgraphicifies an SVM as a maximum margin clgraphicifier .Autor Zach Bedell

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