One of the most important problem that exists in pattern recognition and classification is the development of a decision surface.
Modern algorithms rely on sophisticated statistical methodologies to perform classification.
The simplest approach involves finding the K-nearest neighbor using the Euclidean distance measure. However, Euclidean distance is not the best measure if feature vectors have correlation with unequal variances. To counter this problem, data is transformed to a feature space where the classes are better separated and the features are independent. This makes the distance metric more effective. The signal modeling techniques implemented in this work help define the new feature space.