Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)

Autor:	Morris, Jordan
Rok vydání:	2024
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Artificial Intelligence Computer Science - Machine Learning B.6.0 B.7.0 C.1.0 I.2.6
Druh dokumentu:	Working Paper
Popis:	This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, results demonstrate up to 10% improvement in accuracy, approx. 383X reduction in training time and approx. 86X reduction in inference time. Comment: 6 pages, 12 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.09680 Zobrazit plný text záznamu View this record from Arxiv