The OCON model: an old but gold solution for distributable supervised classification
<|reference_start|>The OCON model: an old but gold solution for distributable supervised classification: 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.<|reference_end|>
@article{giacomelli2024the,
    title={The OCON model: an old but gold solution for distributable supervised
  classification},
    author={Stefano Giacomelli, Marco Giordano, Claudia Rinaldi},
    journal={arXiv preprint arXiv:2410.05320},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05320},
    primaryClass={eess.AS cs.AI cs.CL cs.DB cs.LG cs.SD}
}