Introduction to Support Vector Machines – Part I


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Support Vector Machines were widely used a decade back, but now they have fallen out of favour. The below data from Google trends can establish this more clearly.

Introduction to Support Vector Machines
(Source: Google Trends)

Why did this happen?

As more and more advanced models were developed, support vector machines fell out of favour. It takes a lot of time to train a non-linear kernel, say RBF (Radial Basis Function), of a support vector machine. But they have been found to be very effective in text classification problems. Support Vector Machines (SVMs) are also good at solving non-linear problems with a small dataset.

Support Vector Machines in trading 

When it comes to trading, if you are using daily frequency data, then it is very likely that your data set is extremely limited, probably a few thousand data points. Let us say that you have created a trading strategy using a decision tree to extract a high probability rule from the past data. Now you want to understand how this rule would behave on unseen or test data. Before you try out the SVMs, first let us understand how they work.

Support Vector Machines

A Support Vector Machine is an approach, usually used for performing classification tasks, that uses a separating hyperplane in multidimensional space to perform a given task. Technically speaking, in a p dimensional space, a hyperplane is a flat subspace with p-1 dimensions. For example, In two-dimensions, a hyperplane is a flat one-dimensional subspace or a line. In three dimensions, a hyperplane is a flat two-dimensional subspace that is, a plane. 

If the dimensionality is greater than 3, it can be hard to visualize a hyperplane, but the notion of a p-1 dimensional space still applies.

Stay tuned! Varun will help us understand Support Vector Machines concept with an example.

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