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The Tsetlin Machine (TM) is an interpretable mechanism for patternrecognition that constructs conjunctive clauses from data. The clauses capturefrequent patterns with high discriminating power, providing increasingexpression power with each additional clause. However, the resulting accuracygain comes at the cost of linear growth in computation time and memory usage.In this paper, we present the Weighted Tsetlin Machine (WTM), which reducescomputation time and memory usage by weighting the clauses. Real-valuedweighting allows one clause to replace multiple, and supports fine-tuning theimpact of each clause. Our novel scheme simultaneously learns both thecomposition of the clauses and their weights. Furthermore, we increase trainingefficiency by replacing $k$ Bernoulli trials of success probability $p$ with auniform sample of average size $p k$, the size drawn from a binomialdistribution. In our empirical evaluation, the WTM achieved the same accuracyas the TM on MNIST, IMDb, and Connect-4, requiring only $1/4$, $1/3$, and$1/50$ of the clauses, respectively. With the same number of clauses, the WTMoutperformed the TM, obtaining peak test accuracies of respectively $98.63\%$,$90.37\%$, and $87.91\%$. Finally, our novel sampling scheme reduced samplegeneration time by a factor of $7$.

The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses