[2604.04363] Integer-Only Operations on Extreme Learning Machine Test Time Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.04363 (cs) [Submitted on 6 Apr 2026] Title:Integer-Only Operations on Extreme Learning Machine Test Time Classification Authors:Emerson Lopes Machadoa, Cristiano Jacques Miosso, Ricardo Pezzuol Jacobi View a PDF of the paper titled Integer-Only Operations on Extreme Learning Machine Test Time Classification, by Emerson Lopes Machadoa and 2 other authors View PDF Abstract:We present a theoretical analysis and empirical evaluations of a novel set of techniques for computational cost reduction of test time operations of network classifiers based on extreme learning machine (ELM). By exploring some characteristics we derived from these models, we show that the classification at test time can be performed using solely integer operations without compromising the classification accuracy. Our contributions are as follows: (i) We show empirical evidence that the input weights values can be drawn from the ternary set with limited reduction of the classification accuracy. This has the computational advantage of dismissing multiplications; (ii) We prove the classification accuracy of normalized and non-normalized test signals are the same; (iii) We show how to create an integer version of the output weights that results in a limited reduction of the classification accuracy. We tested our techniques on 5 computer vision datasets commonly used in the literature and the results indicate that our techniques can allow th...