A Novel Multi-step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-63799-6_8
Reference21 articles.
1. Abeyrathna, K.D., Granmo, O.C., Goodwin, M.: Extending the Tsetlin Machine with integer-weighted clauses for increased interpretability. arXiv preprint arXiv:2005.05131 (2020)
2. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);KD Abeyrathna,2019
3. Abeyrathna, K.D., Granmo, O.C., Zhang, X., Jiao, L., Goodwin, M.: The regression Tsetlin Machine - a novel approach to interpretable non-linear regression. Philos. Trans. R. Soc. A 378, 20190165 (2019)
4. Berge, G.T., Granmo, O.C., Tveit, T.O., Goodwin, M., Jiao, L., Matheussen, B.V.: Using the Tsetlin Machine to learn human-interpretable rules for high-accuracy text categorization with medical applications. IEEE Access 7, 115134–115146 (2019). https://doi.org/10.1109/ACCESS.2019.2935416
5. Chen, J., Ran, X.: Deep learning with edge computing: a review. Proc. IEEE 107(8), 1655–1674 (2019)
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