Super deep regression analysis learning method

Abstract

In the current artificial intelligence field, models of deep learning that is prevalent can only map functions. Therefore, a machine learning model with higher performance is desirable. The issue is to construct a machine learning model that enables deep competitive learning between data based on the exact distance. A precise distance scale is submitted by unifying Euclidean space and probability space. It submits a measure of the probability measure of fuzzy event based on this distance. Or, it constructs a new neural network that can transmit information of the maximum probability. Furthermore, super deep competition learning is performed between data having very small ambiguous fuzzy information and minute unstable probability information. By performing integral calculation on this result, it has become possible to obtain dramatic effects at the macro level.

What is claimed is: 
     
         1 . A super deep regression analysis learning method comprising at least one of the following features:
 (1) Self-organization processing of the maximum probability scale processing for distance values from all dots in the given range to straight line of the linear regression; or   (2) To generate a new processing range from the maximum probability scale on both side of the regression line based on the above processing result; or   (3) To determine whether the new processing range is to be expanded or reduced based on the actual dot density within the range, or based on the maximum probability scale.