Sammendrag
Tsetlin Machines learn from input data by creating
patterns in propositional logical, using the literals available in
the data. These patterns vote for the classes in a classification
task. Despite their simplistic premise, Tsetlin machine (TM)s
have been performing at with other popular machine learning
methods across various benchmarks. Not only accuracy, TMs
also perform well in terms of energy efficiency and learning
speed. The general TM scheme works best when there is sufficient
discriminatory information available between two classes. In this
paper, we explore the use of focused negative sampling (FNS) to
discriminate between classes which are not easily distinguishable
from each other. We carry out experiments across diverse classification tasks ranging over natural language processing, image
processing, reinforcement learning to show that this approach
forces the TM to arrive at patterns that can successfully tell
apart two classes that are correlated. Further, we show that
the proposed method achieves accuracy comparable to a vanilla
Tsetlin Machine approach but in approximately 42% less number
of epochs on average
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