Machine Learning Theory And Methods Based On Extension Logic

Machine learning has now been applied to all kinds of intelligent systems. It is the basic means to increase knowledge automatically and improve system's performance purposefully. It is also a significant ability for achieving machine intelligence.Considering the inadequate and limited learning ability of intelligent systems for the time being, and in order to deal with dynamic, complicated and non-structured control and decision systems, especially the incompatible problems in these systems, this paper combines extension logic with machine learning, and proposes the research on machine learning based on extension logic. In this paper, machine learning based on extension logic is studied systemically from the viewpoint of formalization and modeling. Three kinds of basic innovation learning, including similarity learning, inverse learning and learning based on problems, are discussed, and their inference and relevant learning tactics are explored as well.The purpose of the research hopes to raise intelligence standard to some extent for intelligent systems to solve incompatible problems, and thus reach the target to improve the intelligent performance for intelligent systems in the future.The main result of the paper is listed as follows:(1) The paper studies similarity learning. Formalized models for describing similarity is set up, the concepts of δ-similarity and δ-similarity extension elements is introduced, the quantitative calculation for measuring similarity is given, similarity substitution principles for solving incompatible problems are put forward, three basic similarity reasoning rules are developed. The result shows that similarity reasoning is the generalization of analogy. Similarity learning enriches both the content of similarity theory and that of machine learning.(2) The paper studies inverse learning. Four inverse elements are given, the inverse extension is analyzed, the quantitative calculation for measuring inverse solutions is discussed, inverse reasoning is studied and relevant inverse reasoning rules are developed. The result shows that inverse learning can be expanded by inverse extension and inverse reasoning.(3) The paper studies the learning based on problems. Based on extension logic, the representation, analysis, solving and management of problems are discussed. The extension representation of problems, the divergent analysis methods, conjugate analysis methods, key analysis methods and conduction analysis methods are studied. The transforming methods of goal transformation for solving problems, including the upper and lower implicative transformation method, transforming bridge method and critical method, are introduced. Besides the goal transformations and condition transformations, the environment transformations by extension transformations are of significance in solving incompatible...