Through the last year I have been working with support vector machines for a binary text classification task. Having used software such as R and Rapidminer I have not spent much time on understanding what actually goes on inside support vector machines. This I have now started looking into in the hope of getting a better understanding of this classification/regression method.
I have spent a lot of time looking for calculation examples as it tends to enhance my understanding of a concept quite well if I can actually setup a problem in Excel. Therefore I hope to get guidance by asking this question here, as I have not been able to find any step-by-step calculation examples. One can easily find descriptions of the math and optimization problems one need to understand and solve computationally, but a step-by-step calculation example I have not been able to find.
If the forum approves my idea of producing such an example I will do the editing and in the end produce a nice and clear Excel sheet and a guide for future use.
I suggest that we use the Iris dataset (even though it is a multiclass dataset) and simply try to separate Iris setosa from Iris versicolor.
I provide three links. Link one is theory of application of SVMs which I thought one could use as a scaffold. Link two provides a regression example of how I was thinking our product would look in the end. Link three will take you to the Iris dataset.
Below I will try to formulate the problem more neatly.
How can one apply Excel and the technique of a linear support vector machine with soft margins in order to solve a binomial classification task given by separating Iris setosa and Iris versicolor from the Iris dataset using all available features?