{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-tsetlin-machine-a-game-theoretic-bandit","title":"The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic","arxiv_id":"1804.01508","date":"2018-04-04","proceeding":null,"authors":["Ole-Christoffer Granmo"],"abstract":"Although simple individually, artificial neurons provide state-of-the-art\nperformance when interconnected in deep networks. Unknown to many, there exists\nan arguably even simpler and more versatile learning mechanism, namely, the\nTsetlin Automaton. Merely by means of a single integer as memory, it learns the\noptimal action in stochastic environments through increment and decrement\noperations. In this paper, we introduce the Tsetlin Machine, which solves\ncomplex pattern recognition problems with easy-to-interpret propositional\nformulas, composed by a collective of Tsetlin Automata. To eliminate the\nlongstanding problem of vanishing signal-to-noise ratio, the Tsetlin Machine\norchestrates the automata using a novel game. Our theoretical analysis\nestablishes that the Nash equilibria of the game align with the propositional\nformulas that provide optimal pattern recognition accuracy. This translates to\nlearning without local optima, only global ones. We argue that the Tsetlin\nMachine finds the propositional formula that provides optimal accuracy, with\nprobability arbitrarily close to unity. In five benchmarks, the Tsetlin Machine\nprovides competitive accuracy compared with SVMs, Decision Trees, Random\nForests, Naive Bayes Classifier, Logistic Regression, and Neural Networks. The\nTsetlin Machine further has an inherent computational advantage since both\ninputs, patterns, and outputs are expressed as bits, while recognition and\nlearning rely on bit manipulation. The combination of accuracy,\ninterpretability, and computational simplicity makes the Tsetlin Machine a\npromising tool for a wide range of domains. Being the first of its kind, we\nbelieve the Tsetlin Machine will kick-start new paths of research, with a\npotentially significant impact on the AI field and the applications of AI.","url_abs":"http://arxiv.org/abs/1804.01508v10","url_pdf":"http://arxiv.org/pdf/1804.01508v10.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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