8 citations · 20 across the 16 of their papers we have counts for
6 papers · 1 filter
Contracting Tsetlin Machine with Absorbing Automata
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +4
In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both an absorbing Exclude- and an…
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao +3
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domai…
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
Ole-Christoffer Granmo, Per-Arne Andersen, Lei Jiao +3
A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable. If the blanket cannot be reduced without losing…
Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification
Jinbao Zhang, Xuan Zhang, Lei Jiao +3
Neural network-based models have found wide use in automatic long-term electrocardiogram (ECG) analysis. However, such black box models are inadequate for analysing physiological s…
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai +8
Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning acc…
Tsetlin Machine Embedding: Representing Words Using Logical Expressions
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +2
Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations t…