This is the continuation of my series converting the samples found in Machine Learning in Action from Python to F#. After starting on a nice and steady pace, I hit a speed bump with Chapter 6, dedicated to the Support Vector Machine algorithm. The math is more involved than the previous algorithms, and the original Python implementation is very procedural , which both slowed down the conversion to a more functional style.

Anyways, I am now at a good point to share progress. The current version uses Sequential Minimization Optimization to train the classifier, and supports Kernels. Judging from my experiments, the algorithm works - what is missing at that point is some performance optimization.

I’ll talk first about the code changes from the “naïve SVM” version previously discussed, and then we’ll illustrate the algorithm in action, recognizing hand-written digits.

## Main changes from previous version

From a functionality standpoint, the 2 main changes from the previous post are the replacement of the hard-coded vector dot product by arbitrary Kernel functions, and the modification of the algorithm from a naïve loop to the SMO double-loop, pivoting on observations based on their prediction error.

You can browse the current version of the SVM algorithm on GitHub here.

**Injecting arbitrary Kernels**

The code I presented last time relied on vector dot-product to partition linearly separable datasets. The obvious issue is that not all datasets are linearly separable. Fortunately, with minimal change, the SVM algorithm can be used to handle more complex situations, using what’s known as the “Kernel Trick”. In essence, instead of working on the original data, we transform our data in a new space where it is linearly separable:

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