MIQR Active Learning on a Continuous Function and a Discontinuous Function

Active learning balances the cost of data acquisition against its usefulness for training. We select only those data points which are the most informative about the system being modelled. The MIQR (Maximum Inter-Quartile Range) criterion is defined by computing the inter-quartile range of the outputs of an ensemble of networks, and finding the input parameter values for which this is maximal. This method ensures data selection is not unduly influenced by ‘outliers’, but is principally dependent upon the ‘mainstream’ state of the ensemble. MIQR is more effective and efficient than contending methods¹ . The algorithm automatically regulates the training threshold and the network architecture as necessary. We compare active learning methods by applying them to a continuous function and a discontinuous function. Training is more difficult for a discontinuous function than a continuous function, and the volume of data for active learning is substantially less than for passive learning.