Understanding Machine Learning

Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

Type

Book

Intellectual base

Review or monograph

Year

2014

Pages cited

2-6

DOI

10.1017/cbo9781107298019

ISBN

9781107057135

DOI registrant

Cambridge University Press

Link

https://doi.org/10.1017/CBO9781107298019

Cited by

2 citations across 2 artifacts.

Domain-specific

• Learnable Function Class
  • In standard binary classification, finite VC dimension, uniform convergence, ERM learnability, and PAC learnability meet in the fundamental theorem of statistical learning.
• Sample complexity
  • Shalev-Shwartz and Ben-David present this dependency as a central part of learnability rather than a single universal formula.

Verification

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf ×1

Registry ID ref:f26b4920149f · see in the full table