Sammendrag
The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to
address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has
shown competitive performance on MNIST, Fashion-MNIST, and
Kuzushiji-MNIST pattern classification benchmarks, both in terms
of accuracy and memory footprint. In this paper, we propose the
Convolutional Regression Tsetlin Machine (C-RTM) that extends
the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution
operation as in the CTM and then maps the identified patterns
into a real-valued output as in the Regression Tsetlin Machine
(RTM). The C-RTM thus unifies the two approaches. We evaluated
the performance of C-RTM using 72 different artificial datasets,
with and without noise in the training data. Our empirical results
show the competitive performance of C-RTM compared to two
standard CNNs. Additionally, the interpretability of the identified
sub-patterns by C-RTM clauses is analyzed and discussed.
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