There is a new paper out about "Using the Tsetlin Machine to Learn Human -
Interpretable Rules for High - Accuracy Text Categorization with Medical
Applications"  or in our context "…High - Accuracy Text Categorization
of unsourced statements".
Their results on text categorization is quite promising. I've been
wondering why they get so good results, and I suspects it either has to do
with implicit regularization (kind of dropouts), or some other effects I
suspect can be important when you start comparing really good results. One
is that learning binary weights uses less information entropy than learning
weights with higher quantization (more bits), thus with limited training
data more of the available information entropy goes into learning the
actual rules. Another possibility is that the learning algorithm finds
better minimums (actually maximums) than the other algorithms. Ie the
algorithm find stable solutions, that is the real minimum. A third
possibility is that the learning is faster because it does not backprop
(thus more stable and converge faster).
The generated rules are much easier to handle in the users own browser, and
instead of using a central server the text categorization (classification)
can be done in the users own browser. That will make the interaction more
In my opinion this is a neural network. The generated rules can be
reformulated as a disjunctiove normal forms, and then it is more obvious.
There are binary weights, weight multiplication done with and-operators,
and summation done by or-operators.
There are more background in the paper "The Tsetlin Machine - A Game
Theoretic Bandit Driven Approach to Optimal Pattern Recognition with
John Erling Blad