# The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses

arXiv: https://arxiv.org/abs/1911.12607 · HTML: https://mlsanity.com/paper/1911.12607/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space](https://mlsanity.com/paper/2511.19273/) (2025)
- [Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study](https://mlsanity.com/paper/2609.06133/) (2026)

## Abstract (arXiv)

The Tsetlin Machine (TM) is an interpretable mechanism for pattern recognition that constructs conjunctive clauses from data. The clauses capture frequent patterns with high discriminating power, providing increasing expression power with each additional clause. However, the resulting accuracy gain comes at the cost of linear growth in computation time and memory usage. In this paper, we present the Weighted Tsetlin Machine (WTM), which reduces computation time and memory usage by weighting the clauses. Real-valued weighting allows one clause to replace multiple, and supports fine-tuning the impact of each clause. Our novel scheme simultaneously learns both the composition of the clauses and their weights. Furthermore, we increase training efficiency by replacing k Bernoulli trials of success probability p with a uniform sample of average size p k, the size drawn from a binomial distribution. In our empirical evaluation, the WTM achieved the same accuracy as 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 WTM outperformed the TM, obtaining peak test accuracies of respectively 98.63%, 90.37%, and 87.91%. Finally, our novel sampling scheme reduced sample generation time by a factor of 7.