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On the perceptron's compression

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Publication:2106618
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DOI10.1007/978-3-030-51466-2_29OpenAlexW3037931094MaRDI QIDQ2106618

Ido Nachum, Amir Yehudayoff, Itai Panasoff, Shay Moran

Publication date: 16 December 2022

Full work available at URL: https://arxiv.org/abs/1806.05403


zbMATH Keywords

compressionmachine learningconvex separation


Mathematics Subject Classification ID

Theory of computing (68Qxx)



Uses Software

  • Pegasos
  • MNIST


Cites Work

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  • Pegasos: primal estimated sub-gradient solver for SVM
  • Boosting the margin: a new explanation for the effectiveness of voting methods
  • Boosting a weak learning algorithm by majority
  • Large margin classification using the perceptron algorithm
  • An algorithmic theory of learning: Robust concepts and random projection
  • 10.1162/15324430260185600
  • Sample Compression Schemes for VC Classes
  • Extensions of Lipschitz mappings into a Hilbert space
  • On variants of the Johnson–Lindenstrauss lemma
  • On the Generalization Ability of On-Line Learning Algorithms
  • Learning the unlearnable
  • Learning Theory
  • 10.1162/153244303321897681
  • Algorithmic Learning Theory
  • Understanding Machine Learning
  • PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification


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