Sammendrag
Tsetlin machine (TM) is a logic-based machine
learning approach with the crucial advantages of
being transparent and hardware-friendly. While
TMs match or surpass deep learning accuracy for
an increasing number of applications, large clause
pools tend to produce clauses with many literals
(long clauses). As such, they become less interpretable. Further, longer clauses increase the
switching activity of the clause logic in hardware,
consuming more power. This paper introduces a
novel variant of TM learning – Clause Size Constrained TMs (CSC-TMs) – where one can set a
soft constraint on the clause size. As soon as a
clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate
CSC-TM, we conduct classifcation, clustering, and
regression experiments on tabular data, natural language text, images, and board games. Our results
show that CSC-TM maintains accuracy with up to
80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and
BBC Sports. After the accuracy peaks, it drops
gracefully as the clause size approaches a single literal. We fnally analyze CSC-TM power consumption and derive new convergence properties.
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