Focused Negative Sampling for Increased Discriminative Power in Tsetlin Machines

Glimsdal, Sondre; Saha, Rupsa; Bhattarai, Bimal; Giri, Charul; Sharma, Jivitesh; Tunheim, Svein Anders; Yadav, Rohan Kumar

doi:10.1109/istm54910.2022.00021

2022 International Symposium on the Tsetlin Machine (ISTM) 2022

DOI: 10.1109/istm54910.2022.00021

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Focused Negative Sampling for Increased Discriminative Power in Tsetlin Machines

Sondre Glimsdal

Rupsa Saha

Bimal Bhattarai

et al.

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2024

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A Novel Tsetlin Machine with Enhanced Generalization

Anjum,

Zhan

2024

Electronics

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The Tsetlin Machine (TM) is a novel machine learning approach that implements propositional logic to perform various tasks such as classification and regression. The TM not only achieves competitive accuracy in these tasks but also provides results that are explainable and easy to implement using simple hardware. The TM learns using clauses based on the features of the data, and final classification is done using a combination of these clauses. In this paper, we propose the novel idea of adding regularizers to the TM, referred to as Regularized TM (RegTM), to improve generalization. Regularizers have been widely used in machine learning to enhance accuracy. We explore different regularization strategies and their influence on performance. We demonstrate the feasibility of our methodology through various experiments on benchmark datasets.

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A Novel Tsetlin Machine with Enhanced Generalization

Anjum,

Zhan

2024

Electronics

View full text Add to dashboard Cite

show abstract

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Focused Negative Sampling for Increased Discriminative Power in Tsetlin Machines

Cited by 1 publication

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A Novel Tsetlin Machine with Enhanced Generalization

A Novel Tsetlin Machine with Enhanced Generalization

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