Supervised Training Using an Unsupervised Approach to Active Learning
Neural Processing Letters
Selective Learning for Multilayer Feedforward Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
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We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov''s optimal experiment design to the learning problem. We specifically show how to {\it analytically} derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.