A METHOD FOR OPTIMIZING THE CONVERGENCE PERFORMANCE OF DATA LEARNING WITH MINIMAL COMPUTATIONAL STEPS

The present invention relates to a method for optimizing the convergence performance of data learning with minimal computational steps. In this invention, a method for maximizing the convergence efficiency of data learning with limited computational steps is proposed to solve the problem of complexity and learning time. This invented method is useful for enhancing both processing performance and computational speed, and can outperform current unsupervised approaches with a wider breadth of applicability to futuristic applications of big data analytics. Following invention is described in detail with the help of Figure 1 of sheet 1 showing an overview of the research approach with the block-oriented design of KOCM. Sochi Mdia Machine Trasactional Data GntdData Data KOCM Learning Approach Leaning from uslabellcd data QptimltailuiiModeling j using kenlcefcet I Faster Learning approachJ I KOCNI Performance validation 1) Complexity and 2) Convergence Figure1I