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We discuss a new paradigm, called active learning, for supervised learning that aims at improving the efficiency of neural network training procedures. The starting point for active learning is the observation that the traditional approach of randomly selecting training samples leads to large, highly redundant training sets. This redundancy is not always desirable. Especially if the acquisition of...
We present a case study of applying multi-objective optimization techniques to the three dimensional design of a turbine blade in a gas turbine that is designed for use in a small business jet. We illustrate the iterative approach to the formulation of the fitness function that is characteristic for such a practical problem and show how the Pareto front accumulated in this process may serve to represent...
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