|Modern computer algebra systems not only make
calculations (analytic and numeric) easy, but also have good
visualization facilities. Visual demonstrations provide convenient way
to demonstrate the work of the algorithms and help students to
understand them. We consider the problem of binary classification.
Support Vector Machines (SVMs) are attractive from the educational point
of view since it is easy to introduce them gradually: from simple
perceptron to more and more advanced classifiers. We start with the
Rosenblatt’s perceptron – simple binary classifier for linearly
separable cases – and generalize it to maximum margin classifier; then
we introduce kernel trick that allows generalization to nonlinearly
separable cases, and finally accept misclassifications on the training
stage. We model the work of the Rosenblatt’s perceptron and simple SVMs
using Mathematica 6 and Maple 12. Constructed models are used in the
introductory courses on SVMs and Statistical Learning.