Hierarchical based sequencing machine learning model

Abstract

A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method includes employing a machine learning model to predict multiple interdependent output components of an MOD output decision.

Description

technical field [0001] Embodiments discussed herein relate to Hierarchical Based Ranking (HBS) machine learning models. Background technique [0002] Machine learning is a form of artificial intelligence that is used to enable computers to evolve behavior based on empirical data. Machine learning can exploit training examples to capture features of interest for an unknown underlying probability distribution of these examples. Training data can be viewed as examples showing relationships between observed variables. The main focus of machine learning research is: automatic learning to recognize complex patterns and make intelligent decisions based on data. [0003] A major difficulty in machine learning lies in the fact that, given all possible inputs, the set of all possible behaviors is too large to be covered by the set of training data. Therefore, machine learning models must generalize from the training data in order to be able to produce useful outputs in new cases. ...

AI Technical Summary

Problems solved by technology

However, conventional SP cannot handle the interdependencies between different output components

Additionally, traditional SP cannot handle problems with multiple correct output decisions for a given input

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