  The recently introduced Tsetlin Machine (TM) has provided competitive pattern
classification accuracy in several benchmarks, composing patterns with
easy-to-interpret conjunctive clauses in propositional logic. In this paper, we
go beyond pattern classification by introducing a new type of TMs, namely, the
Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference
mechanism of the TM so that input patterns are transformed into a single
continuous output, rather than to distinct categories. 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. The feedback scheme
uses a new activation probability function that stabilizes the updating of
clauses, while the overall system converges towards an accurate input-output
mapping. The performance of the RTM is evaluated using six different artificial
datasets with and without noise, in comparison with the Classic Tsetlin Machine
(CTM) and the Multiclass Tsetlin Machine (MTM). Our empirical results indicate
that the RTM obtains the best training and testing results for both noisy and
noise-free datasets, with a smaller number of clauses. This, in turn,
translates to higher regression accuracy, using significantly less
computational resources.
