Interview with Support Vector Machines Succinctly Author Alexandre Kowalczyk

The following is a short interview with Succinctly series author Alexandre Kowalczyk, whose latest book, Support Vector Machines Succinctly, was published on Monday, October 23. You can download the book here.

 

What should people know about support vector machines? Why are they important?

Support vector machines (SVM) are important because they have been successfully used in three main areas: text categorization, image recognition, and bioinformatics. They are based on a strong mathematical background, and they generalize well. If we see machine learning as a discipline where you need to find the right tool to do the job, then SVMs are very powerful tools. However, they have limitations, and they are often seen as “black boxes,” so it is important to understand them well.

When did you first become interested in support vector machines?

Three years ago, we had a project at work where we needed to classify financial news in real time. The traditional approach using keywords was giving us poor results, so a colleague of mine who had previous experience in natural language processing suggested that we try to use machine learning, support vector machines in particular. I already knew about SVMs because of the machine learning courses I had taken, but this project really made me use them. I learned a lot about how to use them, and I became interested in understanding how and why they work. That is when I started writing my blog svm-tutorial.com where I try to explain the mathematics behind SVMs.

By writing this e-book, did you learn anything new yourself?

I learned a lot because I read many different resources explaining support vector machines. I wanted to read the work of different authors in order to see what was easily explained, and what was not explained in detail. For instance, how to solve an optimization problem with constraints is often suggested as being something secondary when in fact I think it helps the interested reader to really understand how SVMs work. I did not find any real overview of multi-class classification either, so I dedicated a chapter of the book to it and learned about all these different methods. Implementing the sequential minimal optimization algorithm in Python from the pseudocode in the original paper gave me a very good understanding of how it works. If you have time, I recommend writing a book as a great way of learning!

How will support vector machines change over the next few years?

The theory behind SVMs will not change, but maybe we will start seeing them used in other applications. In deep learning, people are trying to incorporate SVMs into convolutional neural networks, while others are researching variations of the original SVM.

Do you see support vector machines as part of a larger trend in software development?

Machine learning is gaining a lot of traction lately and is definitely becoming a big trend in software development today.

What other books or resources on support vector machines do you recommend?

I absolutely recommend the course “Learning from Data,” which explains quite well why learning is possible and has a section on SVMs: work.caltech.edu/telecourse.

For a very mathematical introduction you can read: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Cristianini and Shawe-Taylor. For a friendlier approach, Andrew Ng’s Machine Learning course on Coursera is very good for beginners.

Loading