DATA SCIENCE FOR PATTERN RECOGNITION AND LAW DISCOVERY

Abstract

Pattern recognition is a process of extracting certain features and rules from data, which is achieved with high accuracy by machine learning algorithms such as deep learning. Because the essence of deep learning is interpolation of training data in a feature space, it remains a main issue for data-driven models to obtain extrapolation ability and interpretable computation process. This paper introduces data-driven techniques for law-discovery, which build a natural computational process and obtains generalized prediction ability by discovering laws behind the data, and discuss the current and future trends of the framework.