Investigation of the Sequential Accelerator on the Perceptron for Pattern Recognition

In machine learning methods, when the input data becomes extremely large, the current direct methods
require too large learning times and memory. This project investigates one sequential method to overcome this
problem. It is quite a simple method to implement and is tested using the Perceptron as the base classifier. The
perceptron converges very slowly. So it will be interesting to find out if the proposed accelerator can improve
significantly the computational times of this simple classifier. The student will try various investigations on
different very large data sets and to measure their computational complexities. It has been shown that this
sequential method is very fast and only need a small subset of the large data set to complete the learning.