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Computer Science > Machine Learning

arXiv:1904.12320 (cs)
[Submitted on 28 Apr 2019]

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

Authors:Laurent Boue
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    Abstract: 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.

Subjects: Machine Learning (cs.LG); Discrete Mathematics (cs.DM);
          Information Retrieval (cs.IR); Machine Learning (stat.ML)
Cite as:  arXiv:1904.12320 [cs.LG]
          (or arXiv:1904.12320v1 [cs.LG] for this version)

Submission history

From: Laurent Boue [view email]
[v1] Sun, 28 Apr 2019 13:29:49 UTC (1,374 KB)
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