Real numbers, data science and chaos: How to fit any dataset with a single parameter

Por • 4 may, 2019 • Sección: Ciencia y tecnología

Laurent Boué

We show how any dataset of any modality (time-series, images, sound…) can be approximated by a well-behaved (continuous, differentiable…) scalar function with a single real-valued parameter. Building upon elementary concepts from chaos theory, we adopt a pedagogical approach demonstrating how to adjust this parameter in order to achieve arbitrary precision fit to all samples of the data. Targeting an audience of data scientists with a taste for the curious and unusual, the results presented here expand on previous similar observations regarding expressiveness power and generalization of machine learning models.

arXiv:1904.12320v1 [cs.LG]

Machine Learning (cs.LG); Discrete Mathematics (cs.DM); General Literature (cs.GL); Information Retrieval (cs.IR); Machine Learning (stat.ML)

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