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

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Stefano Giacomelli , Marco Giordano , Claudia Rinaldi ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.05320

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.05320
PDF: https://arxiv.org/pdf/2410.05320
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4403346102

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DOI: https://doi.org/10.48550/arxiv.2410.05320

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

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-10-05

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Authors: Stefano Giacomelli, Marco Giordano, Claudia Rinaldi

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Landing page: https://arxiv.org/abs/2410.05320

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PDF URL: https://arxiv.org/pdf/2410.05320

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Concepts: Computer science

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