I2VM: Incremental import vector machines

Ribana Roscher, Wolfgang Förstner, Björn Waske

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

We introduce an innovative incremental learner called incremental import vector machines (I2VM). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machines, in order to use it for robust incremental learning. By performing incremental update steps, we are able to add and remove data samples, as well as update the current set of model parameters for incremental learning. By using various standard benchmarks, we demonstrate how I2VM is competitive or superior to other incremental methods. It is also shown that our approach is capable of managing concept-drifts in the data distributions.

Original languageEnglish
Pages (from-to)263-278
Number of pages16
JournalImage and Vision Computing
Volume30
Issue number4-5
DOIs
StatePublished - May 2012
Externally publishedYes

Keywords

  • Concept-drifts
  • Import vector machines
  • Incremental learning

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