Classification by Approximation of Normal Distribution for k-neighborhood Data using Steepest Descent Method

Abstract

There is a high probability of the same class of data appearing around the position of parameters of the data manifested in feature space where data parameters are obtained. This study proposes a machine learning method that approximates this using a density function of normal distribution. In addition, if small amounts of data of a different class exist where data of the same class is being collected, the impact of those classes will be significant. However, precision can be improved by determining normal density function by approximation from units of data adjacent to the same class. The proposed technique is designed to speed up learning through semi-optimization using the steepest descent method to optimize the weight applying units of data for approximation and the width of the data distribution of the same class to a variance covariance matrix