As a refinement of logical approaches to inductive learning, we propose category-theoretic inductive
learning. We introduce precedence-inclusion patterns, which generalize constituent structure
trees familiar to computational linguists. Categories of precedence-inclusion patterns have the requisite mathematical structure to support category-theoretic inductive learning. Thus, we obtain structures supporting a significant new theory of pattern generalization that directly speaks to the problem of relational learning in many settings.
By: Frank J. Oles
Published in: RC22387 in 2002
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