Abstract
Mutually-inversistic machine learning is based on inverse hypothetical inference of mutually-inversistic logic constructed by the author. In mutually-inversistic logic, there are 8 kind of hypothetical inferences, all of which are from the major premise and the minor premise to infer the conclusion. For each hypothetical inference, there are 2 inverse hypothetical inferences, one is from the minor premise and the conclusion to the major premise, the other is from the major premise and the conclusion to the minor premise. Therefore, there are 16 kind of mutually-inversistic machine learning models altogether. FOIL, decision tree learning in inductive learning, rough set learning, genetic algorithm, inverse resolution in inductive logic programming are special cases of mutually-inversistic machine learning.
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