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.
|Title of host publication||Proceedings of the Seventh Australian Conference on Neural Networks|
|Editors||Peter Bartlett, Anthony Burkitt, Robert C. Williamson|
|Place of Publication||Canberra|
|Number of pages||5|
|Publication status||Published - 1996|
|Event||Australian Conference on Neural Networks (7th : 1996) - Canberra, Australia|
Duration: 10 Apr 1996 → 12 Apr 1996
|Conference||Australian Conference on Neural Networks (7th : 1996)|
|Period||10/04/96 → 12/04/96|
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.