Construction of Specific Machine Learning Paradigms from a Primitive-Based Generic Machine Learning Model
نویسندگان
چکیده
This paper is about the construction of various specific machine learning paradigms using a primitive-based generic machine learning model. The generic model identifies five functional components involved in a machine learning process, including an input, a transformation, a control, an output, and a knowledge base. It also identifies a set of basic machine learning mechanisms for each component to perform primitive learning activities. Through proper integration of the primitive mechanisms in each component, various machine learning paradigms can be constructed, including EBL-GMLM as an explanation-based learning paradigm, VS-GMLM as a version-space-based learning paradigm, and IEA-GMLM as a multi-strategy combining learning paradigm of analytical
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