Structural Learning of Boltzmann Machine: An Application

摘要

In order to solve a problem efficiently, we propose the structural learning of Boltzmann machine. The proposed method enables us to solve the problem defined in terms of mixed integer quadratic programming. In this research, an analysis is performed by using the concepts of the reliability and risks of units evaluated using a variance-covariance matrix and also the effect and expanses of replacement are measured. Mean-variance analysis is formulated as a mathematical programming with two objectives to minimize the risk and maximize the expected return. Finally, we employ a Boltzmann machine to solve the mean-variance analysis efficiently. At the end, the result of our method was exemplified. This method enables us to obtain a more effective selection of results and enhanced the effectiveness of the decision making process.