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Perceptrons Under Verifiable Random Data Corruption

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Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

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Abstract

We study perceptrons when datasets are randomly corrupted by noise and subsequently such corrupted examples are discarded from the training process. Overall, perceptrons appear to be remarkably stable; their accuracy drops slightly when large portions of the original datasets have been excluded from training as a response to verifiable random data corruption. Furthermore, we identify a real-world dataset where it appears to be the case that perceptrons require longer time for training, both in the general case, as well as in the framework that we consider. Finally, we explore empirically a bound on the learning rate of Gallant’s “pocket” algorithm for learning perceptrons and observe that the bound is tighter for non-linearly separable datasets.

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Acknowledgements

Part of the work was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma. The work was supported by the second author’s startup fund. The first author worked on this topic while he was an undergraduate McNair Sholar.

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Authors and Affiliations

  1. University of Oklahoma, Norman, USA

    Jose E. Aguilar Escamilla & Dimitrios I. Diochnos

Authors
  1. Jose E. Aguilar Escamilla
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  2. Dimitrios I. Diochnos
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Corresponding authors

Correspondence to Jose E. Aguilar Escamilla or Dimitrios I. Diochnos .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Escamilla, J.E.A., Diochnos, D.I. (2024). Perceptrons Under Verifiable Random Data Corruption. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53968-8

  • Online ISBN: 978-3-031-53969-5

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