Modified Incremental learning (ensemble) System with Weight Updating Rule

Discrimination is a method of finding models that define and compare mathematical classes or principles. To use the model to predict the beauty of objects of unknown size. Using an ensemble system is one of the best-known methods of clustering. Acceleration is one of the many classification techniques. It was originally intended to increase the accuracy of weaker subjects by selecting study sets for weaker classifiers based on the individual's overall memory function.Learn++ uses the relative representation power of a set of classifiers to learn additional facts from new data. Learn++ can learn new lessons without forgetting existing knowledge. Even if previously used data is no longer available. Mathematical substitution rules based on all functions of common sense. For choosing a course for the next weak classifier. As the optimal probability of remembering increases k', new learning may occur. An individual-based distribution rotation rule guarantees stability and prevents deterioration in overall efficiency. While partition rotation rules based on shared logic allow for green incremental control capabilities. I have a new algorithm where the right combination of people and ideas can lead to better performance.