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Doctoral thesis
Open access
English

Sparse learning for variable selection with structures and nonlinearities

ContributorsGregorova, Magda
Defense date2018-11-12
Abstract

In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on a limited set of input variables the models naturally counteract the overfitting problem ubiquitous in learning from finite sets of training points. Sparse models are cheaper to use for predictions, they usually require lower computational resources and by relying on smaller sets of inputs can possibly reduce costs for data collection and storage. Sparse models can also contribute to better understanding of the investigated phenomenons as they are easier to interpret than full models.

eng
Keywords
  • Machine learning
  • Variable selection
  • Kernel methods
  • Granger causality
Citation (ISO format)
GREGOROVA, Magda. Sparse learning for variable selection with structures and nonlinearities. 2018. doi: 10.13097/archive-ouverte/unige:115678
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Technical informations

Creation04/02/2019 8:42:00 AM
First validation04/02/2019 8:42:00 AM
Update time03/15/2023 4:04:48 PM
Status update03/15/2023 4:04:48 PM
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