Learning of Compositional Hierarchies by Data-Driven Chunking

Karl Pfleger, Stanford University

Compositional hierarchies (CHs), layered structures of part-of relationships, underlie many forms of data, and rep-resentations involving these structures lie at the heart of much of AI. Despite this importance, methods for learning CHs from data are scarce. We present an unsupervised technique for learning CHs by an on-line, bottom-up chunking process. At any point, the induced structure can make predictions about new data.

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