Active Learning for Monitoring Network Optimization

Devis Tuia*, Alexei Pozdnoukhov, Loris Foresti, Mikhail Kanevski

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Kernel-based active learning strategies were studied for the optimization of environmental monitoring networks. This chapter introduces the basic machine learning algorithms originated in the statistical learning theory of Vapnik (1998). Active learning is closer to an optimization done using sequential Gaussian simulations. The chapter presents the general ideas of statistical learning from data. It derives the basics of kernel-based support vector algorithms. The active learning framework is presented and machine learning extensions for active learning are described in the chapter. Kernel-based active learning strategies are tested on real case studies. The chapter explores the use of a committee of machines to characterize the effect of sampling uncertainty to predict the uncertainty. Future studies have to be targeted towards the development of multi-objective monitoring network design algorithms to measure complex, nonlinear and high-dimensional environmental processes that vary in space and time.

Original languageEnglish
Title of host publicationSpatio-temporal Design
Subtitle of host publicationAdvances in Efficient Data Acquisition
EditorsJorge Mateu, Werner G. Muller
PublisherWiley
Pages285-318
Number of pages34
ISBN (Electronic)9781118441862
ISBN (Print)9780470974292
DOIs
Publication statusPublished - 11 Oct 2012
Externally publishedYes

Keywords

  • Active learning
  • Gaussian simulations
  • Kernel-based support vector algorithms
  • Machine learning
  • Monitoring network optimization

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