Performance Improvement of Multilayer Perceptrons with Increased Output Nodes

Performance Improvement of Multilayer Perceptrons with Increased Output Nodes per Class

Generally, we allocate one output node per class in pattern recognition applications of MLPs(multilayer perceptrons). In this paper, we propose a method to improve generalization capability of MLPs through increasing the number of output nodes per class. We verify that the proposed method decreases misclassification ratios of MLPs through a short mathematical aspect. And then, simulations of is...

Quantile regression with multilayer perceptrons

We consider nonlinear quantile regression involving multilayer perceptrons (MLP). In this paper we investigate the asymptotic behavior of quantile regression in a general framework. First by allowing possibly non-identifiable regression models like MLP's with redundant hidden units, then by relaxing the conditions on the density of the noise. In this paper, we present an universal bound for the...

Fast training of multilayer perceptrons

Training 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 output activation functions, direct soluti...

Hyperparameter Optimization with Factorized Multilayer Perceptrons

In machine learning, hyperparameter optimization is a challenging task that is usually approached by experienced practitioners or in a computationally expensive brute-force manner such as grid-search. Therefore, recent research proposes to use observed hyperparameter performance on already solved problems (i.e. data sets) in order to speed up the search for promising hyperparameter configuratio...

Laplacian pyramid with multilayer perceptrons interpolators