Accurate modelling with minimised data collection - an active learning algorithm

Tirthankar Raychaudhuri, Leonard Hamey

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

A data gathering method based on active querying is described. In this method data is reduced to a minimum, yet modelling accuracy is uncompromised. Our active querying criterion is determined by whether or not several neural network models agree when they are fitted to random subsamples of a small amount of collected data. Experiments have established the feasibility of our algorithm. It is also shown that our approach results in a more samples being collected in the neighbourhood of the more significant inputs.
Original languageEnglish
Title of host publicationProceedings of the Seventh Australian Conference on Neural Networks
EditorsPeter Bartlett, Anthony Burkitt, Robert C. Williamson
Place of PublicationCanberra
PublisherANU
Pages11-15
Number of pages5
ISBN (Print)0731524292
Publication statusPublished - 1996
EventAustralian Conference on Neural Networks (7th : 1996) - Canberra, Australia
Duration: 10 Apr 199612 Apr 1996

Conference

ConferenceAustralian Conference on Neural Networks (7th : 1996)
CountryAustralia
CityCanberra
Period10/04/9612/04/96

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  • Cite this

    Raychaudhuri, T., & Hamey, L. (1996). Accurate modelling with minimised data collection - an active learning algorithm. In P. Bartlett, A. Burkitt, & R. C. Williamson (Eds.), Proceedings of the Seventh Australian Conference on Neural Networks (pp. 11-15). Canberra: ANU.