The OCON model: an old but gold solution for distributable supervised classification

• URL: http://arxiv.org/abs/2410.05320v1
• Date: Sat, 5 Oct 2024 09:15:01 GMT
• Title: The OCON model: an old but gold solution for distributable supervised classification
• Authors: Stefano Giacomelli, Marco Giordano, Claudia Rinaldi,
• Abstract summary: This paper introduces a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks. We achieve classification accuracy comparable to nowadays complex architectures (90.0 - 93.7%)
• Score: 0.28675177318965045
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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