Statistical Learning Theory
VC dimension, Rademacher complexity, and PAC-Bayes.
Statistical Learning Theory. VC dimension, Rademacher complexity, and PAC-Bayes.
Foundations and canonical references
The standard treatments of statistical learning theory approach the subject from complementary angles. Vapnik, Statistical Learning Theory (1998) provides historical context and an early systematic exposition of the material. Mohri, Foundations of Machine Learning (2018) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to.
Open methodological questions for statistical learning theory include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · historical · 1998Statistical Learning Theoryvapnik-1998
- textbook · primary · 2018Foundations of Machine Learningmohri-2018, rostamizadeh-2018, talwalkar-2018
In context
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