High Performance and Lightweight Single Semi-Lattice Algebraic Machine Learning

Algebraic machine learning is a novel parameter-free model that has demonstrated impressive accuracy in challenging tasks such as the MNIST dataset and N-Queens completion. However, its utilization of two semi-lattices can lead to significant computational demands. To tackle this issue, a solution h...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Haidar, Imane M., Sliman, Layth, Damaj, Issam W., Haidar, Ali M.
Format: Article
Language:English
Subjects:
Accuracy
Algebra
Algebraic machine learning
Artificial Intelligence
Computer Science
data conceptualisation
Datasets
distributed artificial intelligence
Energy efficiency
Error analysis
error function minimization
generalization
Handwriting
High performance computing
Lattices
Lattices (mathematics)
Lightweight
Machine learning
Memory
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Summary:Algebraic machine learning is a novel parameter-free model that has demonstrated impressive accuracy in challenging tasks such as the MNIST dataset and N-Queens completion. However, its utilization of two semi-lattices can lead to significant computational demands. To tackle this issue, a solution has been proposed that employs a single semi-lattice model, resulting in reduced memory requirements and improved efficiency. This research endeavors to bridge the gap between algebraic concepts and programming concepts, thereby making them more accessible to a broader range of researchers. This paper presents the development of a lightweight single semi-lattice algebraic model. The implementation achieved remarkable accuracy on the MNIST dataset of handwritten digits, with an error rate of 2.7%, a False Positive Rate (FPR) of 2.5%, and a False Negative Rate (FNR) of 4.4%. This work holds great significance due to its ability to explain the algebraic model in a simpler manner compared to the original work, while also serving as a proof of concept. Additionally, it studies the memory and time performance of the novel model with an average of 383 MB and 3 hours per digit.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3376525