Inductive Inference by Using Information Compression

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Abstract

Inductive inference is of central importance to all scientific inquiries. Automating the process of inductive inference is the major concern of machine learning researchers. This article proposes inductive inference techniques to address three inductive problems: (1) how to automatically construct a general description, a model, or a theory to describe a sequence of observations or experimental data, (2) how to modify an existing model to account for new observations, and (3) how to handle the situation where the new observations are not consistent with the existing models. The techniques proposed in this article implement the inductive principle called the minimum descriptive length principle and relate to Kolmogorov complexity and Occam's razor. They employ finite state machines as models to describe sequences of observations and measure the descriptive complexity by measuring the number of states. They can be used to draw inference from sequences of observations where one observation may depend on previous observations. Thus, they can be applied to time series prediction problems and to one-to-one mapping problems. They are implemented to form an automated inductive machine.

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