Friday, February 01, 2008

My AI mini project

UCEC3064 - Intelligent Technique for Engineering Applications

Proposed mini project title:
Improving Circular Pairwise Neural Network Performance on Multiclass Classification Problems

Traditionally, neural networks are trained in a monolithic fashion. That is, a neural network would be trained to classify all K classes, preferably given equivalent amount of training samples for each classes. Training is slow and is unable to take advantage of current multi-cores or multi-processor (SMP) systems to train different classes concurrently. The research done by Teo Choon Hui on circular pairwise classification is an attempt to remedy this by breaking down a K class problem into k binary circular pairwise classification sub-problems. However, this method will reduce recognition accuracy due to the fact that there is a lacked of direct competition between certain pairs of classes. But, at the same time, such a method reduces training time by almost a factor of 3. In this research, we will compare the results of a binary one-versus-all classifier and a pairwise classifier with a circular pairwise classifier as proposed by Teo Choon Hui and attempts to improve recognition accuracy by experimenting with selective circular pairwise classification, in which hard binary sub-problems are paired instead of randomly choosing any 2 classes. We will then attempts to introduce an N-th class into each binary classifier to become a ternary circular pairwise classifier and evaluate its performance.

One-versus-all, Binary classification, Pairwise, Single-winner election methods, Circular Pairwise