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Multilayer perceptrons for classification and regression
Neurocomputing, 1991Abstract We review the theory and practice of the multilayer perceptron. We aim at addressing a range of issues which are important from the point of view of applying this approach to practical problems. A number of examples are given, illustrating how the multilayer perceptron compares to alternative, conventional approaches.
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Fast training of multilayer perceptrons
IEEE Transactions on Neural Networks, 1997Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This paper describes a new approach which is much faster and certain than error backpropagation. The proposed approach is based on combined iterative and direct solution methods. In this approach, we use an inverse transformation for linearization of nonlinear
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Multilayer Perceptron for Label Ranking
2012Label Ranking problems are receiving increasing attention in machine learning. The goal is to predict not just a single value from a finite set of labels, but rather the permutation of that set that applies to a new example (e.g., the ranking of a set of financial analysts in terms of the quality of their recommendations).
Geraldina Ribeiro +3 more
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An upper bound on the variance of scalar multilayer perceptrons for log-concave distributions
Neurocomputing, 2022Aydin Sarraf
exaly
Fuzzy Multilayer Perceptrons for Fuzzy Vector Regression
Women in Engineering and Science, 2022Sansanee Auephanwiriyakul +2 more
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On the relations between discriminant analysis and multilayer perceptrons
Neural Networks, 1991P Gallinari, S Thiria, F Badran
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