How can one set up a linear support vector machine in Excel? 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.
Theory and application of SVMs
A guide for regressions
Link for the Iris dataset
Below I will try to formulate the problem more neatly.
Problem description:

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?

 A: Honestly, I am not sure why you want to do this in Excel. Nonetheless, ...
A linear SVM requires solving a quadratic program with several linear constraints. You can check this answer [1] to find out how the quadratic program is setup. Once you setup the quadratic program and find a solver that can help you solve it in Excel, then you are good to go.
On the other hand, the corresponding quadratic program has a dual that gives rise to the notion of kernels. The objective function for the dual can be found here [2]. If you can find a quadratic program solver in Excel, you might as well solve the dual, which will allow you to solve problems beyond linear kernels.
If you don't have a QP solver at hand, then you can write the SMO algorithm [3] which solves the SVM dual. The provided link gives you a pseudocode. SMO is one of the simplest algorithms to solve the SVM dual, but also the slowest. For a small number of training data, it should be pretty fast, however.
[1] Given a set of points in two dimensional space, how can one design decision function for SVM?
[2] Non-linear SVM classification with RBF kernel
[3] http://cs229.stanford.edu/materials/smo.pdf
A: This looks like a good tutorial, and has a downloadable Excel example:
http://people.revoledu.com/kardi/tutorial/Regression/KernelRegression/KernelRegression.htm
A: You might try using Excel2SVM if you want to organize your data in an excel format.  http://www.bioinformatics.org/Excel2SVM/ could be helpful 
A: You can find a tutorial here, it uses Excel (no macros) and explains everything in an intuitive way (beware: most parts are behind a paywall, but the price is reasonable):
http://people.revoledu.com/kardi/tutorial/SVM/index.html
