# Data Warehousing and Data Science

## 24 May 2021

### SVM with RBF Kernel

Filed under: Machine Learning — Vincent Rainardi @ 5:20 pm
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Out of all machine learning algorithms, SVM with RBF Kernel is the one that fascinates me the most. So in this article I am going to try to explain what it is, and why it works wonders.

I will begin by explaining a problem, and how this algorithm solves that problem.

Then I will explain what it is. SVM = Support Vector Machine, and RBF = Radial Basis Function. So I’ll explain what a support vector is, what a support vector machine is, what a kernel is and what a radial basis function is. Then I’ll combine them all and give a overall picture of what SVM with RBF Kernel is.

After we understand what it is, I’m going to briefly explain how it works.

Ok let’s start.

The Problem

We need to clasify 1000 PET scan images into cancer and benign. Whether it is cancer or benign is affected by two variable, X and Y. Fortunately the cancer and benign scans are linearly separable like this:

Figure 1. Linearly separable cancer and benign scans

We call this space a “linear space”. In this case we can find the equation of a line* which separate cancer and benign scans. Because it is linearly separable we can use linear machine learning algorithms.

The problem is when the data set is not linearly separable, like this:

Figure 2. Cancer and benign scans which are not linearly separable

In this case it is separable by an ellipse. We call this space a non linear space. We can find the equation for the ellipse but it won’t work with linear machine learning algorithms.

The Solution

The solution to this problem is to transform the non linear space into a linear space, like this:

Figure 3. Transforming a non-linear space into a linear space

Once it is in a linear space, we can use linear machine learning algorithms.

Why is it important to be able to use a linear ML algorithms? Because there are many popular linear ML algorithms which work well.

What is a Support Vector?

The 4 data points A, B, C, D in figure 4 below are called support vectors. They are the data points located nearest to the separator line.

Figure 4. Support Vectors

They are called support vector because they are the ones which determine where the separator line is located. The other data points don’t matter, they don’t affect where the spearator line is located. Even if we remove all the other data points the separator line will still be the same, as illustrated below:

Figure 5. Support Vectors affect the separator line

What is a Support Vector Machine?

Support Vector Machine is a machine learning algorithm which uses the support vector concept above to classify data. One of the main features of SVM is that it allows some data points to be deliberately misclassified, in order to achieve a higher overall accuracy.

Figure 6. SVM deliberately allows misclassifiction

In figure 6, data point A is deliberately misclassified. The SVM algorithm ignores data point A, so it can better classify all the other data points. As a result it achieves better overall accuracy, compared to if it tries to include data point A. This principle makes SVM work well when the data is partially intermingled.

What is a Kernel?

A kernel is a transformation from one space to another. For example, in figure 7 we transform the data points from variable X and Y to variable R and T. We can take variable R for example as “the distance from point A”.

Figure 7 Kernel – transforming data from one space to another

To be more precise, transformation like this is called a “Kernel Function” not just a Kernel.

Radial function is a function whose value depends on the distance from the point of origin (x = 0 and y = 0).

For example if the distance to origin is constant, then it is a circle if we plot it on X and Y axes. In figure 8 below we can see a circle with r = 2, where r is the distance from origin. In this case r is the radius of the circle and point O is the point of origin.

What is a Radial Basis Function (RBF)?

A Radial Basis Function (RBF) is a radial function where the reference point is not the origin. For example, distance of 3 from point (5,5) is like this:

We can sum multiple RBFs to get shapes with multiple centres like this:

So what is SVM with RBF Kernel?

SVM with RBF Kernel is a machine learning algorithm which is capable to classify data points separated with radial based shapes like this:

Figure 11 SVM with RBF kernel
(source: http://qingkaikong.blogspot.com/2016/12/machine-learning-8-support-vector.html)

And that ability in machine learning is amazing because it can “hug” the data points closely, precisely separating them out.

References