Neural Processing Letters

, Volume 15, Issue 3, pp 247–260 | Cite as

Supervised Training Using an Unsupervised Approach to Active Learning

  • A. P. Engelbrecht
  • R. Brits
Article

Abstract

Active learning algorithms allow neural networks to dynamically take part in the selection of the most informative training patterns. This paper introduces a new approach to active learning, which combines an unsupervised clustering of training data with a pattern selection approach based on sensitivity analysis. Training data is clustered into groups of similar patterns based on Euclidean distance, and the most informative pattern from each cluster is selected for training using the sensitivity analysis incremental learning algorithm in (Engelbrecht and Cloete, 1999). Experimental results show that the clustering approach improves on standard active learning as presented in (Engelbrecht and Cloete, 1999).

active learning clustering incremental learning pattern informativeness sensitivity analysis 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • A. P. Engelbrecht
    • 1
  • R. Brits
    • 1
  1. 1.Department of Computer ScienceUniversity of PretoriaPretoriaSouth Africa

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