Neural network based class-conditional probability density function using kernel trick for supervised classifier

The practical limitation of the Bayes classifier used in pattern recognition is computing the class-conditional probability density function (pdf) of the vectors belonging to the corresponding classes. In this paper, a neural network based approach is proposed to model the class-conditional pdf, which can be further used in supervised classifier (e.g. Bayes classifier). It is also suggested to use kernel version (using kernel trick) of the proposed approach to obtain the class-conditional pdf of the corresponding training vectors in the higher dimensional space. This is used for better class separation and hence better classification rate is achieved. The performance of the proposed technique is validated by using the synthetic data and the real data. The simulation results show that the proposed technique on synthetic data and the real data performs well (in terms of classification accuracy) when compared with the classical Fisher׳s Linear Discriminant Analysis (LDA) and Gaussian based Kernel-LDA.