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Found 8 papers, 6 papers with code
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
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.
On the Convergence of Tsetlin Machines for the XOR Operator
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks.
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.
A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata to a Nash Equilibrium of the TM game.
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability
Here, we address the accuracy-interpretability challenge in machine learning by equipping the TM clauses with integer weights.
A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation
Although the RTM has solved non-linear regression problems with competitive accuracy, the resolution of the output is proportional to the number of clauses employed.
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting.
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and normalization mechanism; and (3) employing a feedback scheme that updates the TM clauses to minimize the regression error.
