The OCON model: an old but gold solution for distributable supervised classification
Abstract
This paper introduces to a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, specifically addressing a vowel phonemes classification case study within the Automatic Speech Recognition research field. Through pseudo-Neural Architecture Search and Hyper-Parameters Tuning experiments conducted with an informed grid-search methodology, we achieve classification accuracy comparable to nowadays complex architectures (90.0 - 93.7%). Despite its simplicity, our model prioritizes generalization of language context and distributed applicability, supported by relevant statistical and performance metrics. The experiments code is openly available at our GitHub.
Cite
@article{arxiv.2410.05320,
title = {The OCON model: an old but gold solution for distributable supervised classification},
author = {Stefano Giacomelli and Marco Giordano and Claudia Rinaldi},
journal= {arXiv preprint arXiv:2410.05320},
year = {2025}
}
Comments
Accepted at "2024 29th IEEE Symposium on Computers and Communications (ISCC): workshop on Next-Generation Multimedia Services at the Edge: Leveraging 5G and Beyond (NGMSE2024)". arXiv admin note: text overlap with arXiv:2410.04098