Query learning based on boundary search and gradient computation of trained multilayer perceptrons

A novel approach to query-based neural network learning is presented. A layered perceptron partially trained for binary classification is considered. The single-output neuron is trained to be either a 0 or a 1. A test decision is made by thresholding the output at, for example, 1/2. The set of inputs that produce an output of 1/2 forms the classification boundary. For each boundary point, the classification gradient can be generated. The gradient provides a useful measure of the sharpness of the multidimensional decision surfaces. Conjugate input pair locations are generated using the boundary point and gradient information and are presented to the oracle for proper classification. These new data are used to further refine the classification boundary, thereby increasing the classification accuracy. The result can be a significant reduction in the training set cardinality in comparison with, for example, randomly generated data points. An application example to power security assessment is given