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Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the component networks predictions to produce the ensemble...
Appropriate methods are helpful to measure risk. The risk is objective in acquisitions. It resources from subjective risk, morality risk, information asymmetry risk, law risk, system risk, policy risk and market risk during the process of mergers and acquisitions decision-making. In the different stages of decision-making, the emphases of risks are different Index of measuring risks can be established...
For a given data set, different learning algorithms typically provide different classifiers. Although it is possible to simply select the most successful classifier, the less successful classifiers could have potentially valuable information that may be wasted. This work proposes GAESC, an algorithm for evolving a set of classifiers into a single symbolic classifier using genetic algorithms. Individuals...
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