Mathematical foundations for supervised learning

Mark Hoogendoorn*, Burkhardt Funk

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

In this chapter the theoretical underpinning of supervised learning are discussed. The whole supervised machine learning process is explained from a more formal perspective as well as some underlying theories. The theories discussed include concepts such as PAC learnability and VC dimensions. The implications of these theories are discussed.

Original languageEnglish
Title of host publicationMachine Learning or the Quantified Self
Subtitle of host publicationOn the Art of Learning from Sensory Data
PublisherSpringer/Verlag
Chapter6
Pages101-121
Number of pages21
Volume35
ISBN (Electronic)9783319663081
ISBN (Print)9783319663074
DOIs
Publication statusE-pub ahead of print - 27 Sep 2017

Publication series

NameCognitive Systems Monographs
Volume35
ISSN (Print)1867-4925
ISSN (Electronic)1867-4933

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  • Cite this

    Hoogendoorn, M., & Funk, B. (2017). Mathematical foundations for supervised learning. In Machine Learning or the Quantified Self: On the Art of Learning from Sensory Data (Vol. 35, pp. 101-121). (Cognitive Systems Monographs; Vol. 35). Springer/Verlag. https://doi.org/10.1007/978-3-319-66308-1_6