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Four-Layer Distance Metric and Distance-based Kernel Functions for Inductive Logic Programming

Nirattaya Khamsemanan, Cholwich Nattee, Masayuki Numao


Inductive Logic Programming (ILP) is a field of study focusing developingmachine learning algorithms using logic programming to describe examples andhypotheses. This makes ILP techniques capable to deal with relational data,i.e. non-vector data. To learn from ILP data, an algorithm must be able tohandle non-linear data. Hypotheses generated from ILP techniques are in form ofHorn clauses, which can be interpreted by human. This is a benefit overconventional learning algorithms that generate black-box hypotheses orclassification models. Nevertheless, learning algorithms used by ILP techniquesare based on covering algorithms. It requires high comput

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The Thai Journal of Mathematics organized and supported by The Mathematical Association of Thailand and Thailand Research Council and the Center for Promotion of Mathematical Research of Thailand (CEPMART).

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|ISSN 1686-0209|