Predictive modeling without notion of time

Mark Hoogendoorn*, Burkhardt Funk

*Corresponding author for this work

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

Abstract

Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are provided on how to apply these algorithms to quantified self data, including the learning setup (e.g. learning for single users or across multiple users) and other practical considerations such as feature selection and regularization. Data stream mining approaches for predictive modeling are also briefly discussed.

Original languageEnglish
Title of host publicationCognitive Systems Monographs
PublisherSpringer/Verlag
Chapter7
Pages123-165
Number of pages43
Volume35
ISBN (Electronic)9783319663081
ISBN (Print)9783319663074
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

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

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  • Cite this

    Hoogendoorn, M., & Funk, B. (2018). Predictive modeling without notion of time. In Cognitive Systems Monographs (Vol. 35, pp. 123-165). (Cognitive Systems Monographs; Vol. 35). Springer/Verlag. https://doi.org/10.1007/978-3-319-66308-1_7