Book Description
In this chapter, we develop a Bayesian Pairwise Classifier framework that is suitable for pattern recognition problems involving a moderately large number of classes, and apply it to two character recognition datasets. A C class pattern recognition problem (e.g.; C = 26 for recognition of English Alphabet) is divided into a set of (2C) two-class problems. For each pair of classes, a Bayesian classifier based on a mixture of Gaussians (MOG) is used to model the probability density functions conditioned on a single feature. A forward feature selection algorithm is then used to grow the feature space, and an efficient technique is developed to obtain a MOG in the larger feature space from the MOG's in the smaller spaces. Apart from improvements in classification accuracy, the proposed architecture also provides valuable domain knowledge such as identifying what features are most important in separating a pair of characters, relative distance between any two characters, etc.
