Definition and purpose of an SVM

I support Vector Machines, do you?

In the field of machine learning, SVMs are similarly recognized as support vector networks and are defined as supervised learning models with accompanying learning algorithms that analyze data used for classification.

An important note about SVMs is that they are all about the ability to successfully perform pattern recognition. In other words, SVMs promote the ability to extend patterns found in data that are:

Not linearly separable by transformations of original data to map into new space.

Again, everything you will find and come to know about SVMs will align with the idea that an SVM is a supervised machine learning algorithm which is most often used for classification or regression problems in statistics.

The trick

You will hear most data scientists today refer to the trick or the SVM Trick; what they are referring to is that support vector machine algorithms use a method referred to as the kernel trick.

The kernel trick transforms...