A two component mixture model for density estimation and classification
Abstract Density estimation is one of the most important techniques that have been used to solve machine learning problems. The mixture model is a useful tool for density estimation and classification. In this paper, we describe a simple two component mixture model for density estimation. The associated EM algorithm for carrying out maximum likelihood estimation is also proposed. An application of the proposed model and approach to discriminant the well known data from the Wisconsin breast cancer study is demonstrated. The experimental results show that the proposed model provides a good way to the classification problems.