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MMLD Inference of Multilayer Perceptrons
2013A multilayer perceptron comprising a single hidden layer of neurons with sigmoidal transfer functions can approximate any computable function to arbitrary accuracy. The size of the hidden layer dictates the approximation capability of the multilayer perceptron and automatically determining a suitable network size for a given data set is an interesting ...
Enes Makalic, Lloyd Allison
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An Enhanced Fuzzy Multilayer Perceptron
2004Error back-propagation algorithm of the multilayer perceptron may result in local-minima because of the insufficient nodes in the hidden layer, inadequate momentum set-up, and initial weights. In this paper, we proposed the fuzzy multilayer perceptron which is composed of the ART1 and the fuzzy neural network.
Kwang-Baek Kim, Choong Shik Park
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An Approach to Encode Multilayer Perceptrons
2002Genetic connectionism is based on the integration of evolution and neural network learning within one system. An overview of the Multilayer Perceptron encoding schemes is presented. A new approach is shown and tested on various case studies. The proposed genetic search not only optimizes the network topology but shortens the training time.
Jerzy Korczak 0001, Emmanuel Blindauer
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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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Interactive initialization of the multilayer perceptron
Pattern Recognition Letters, 2000Abstract A new multilayer preceptor initialization method is proposed and compared experimentally with a traditional random initialization method. An operator maps training-set vectors into a two-variate space, inspects bi-variate training-set vectors and controls the complexity of the decision boundary.
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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
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Fuzzy Multilayer Perceptrons for Fuzzy Vector Regression
Women in Engineering and Science, 2022Sansanee Auephanwiriyakul +2 more
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