Results 211 to 220 of about 27,824 (260)

Kernel Multilayer Perceptron

2011 24th SIBGRAPI Conference on Graphics, Patterns and Images, 2011
We enhance the Multi layer Perceptron to map a feature vector not only from the original d-dimensional feature space, but from an intermediate implicit Hilbert feature space in which kernels calculate inner products. The kernel substitutes the usual inner product between weight vectors and the input vector (or the feature vector of the hidden layer ...
Thomas W. Rauber, Karsten Berns
openaire   +1 more source

Adaptive multilayer perceptrons

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
Multilayer perceptrons (MLP) with long- and short-term memories (LASTM) are proposed for adaptive processing. The activation functions of the output neurons of such a network are linear and thus the weights in the last layer affect the outputs of the network linearly and are called linear weights.
James T. Lo, Devasis Bassu
openaire   +1 more source

Robustness in Multilayer Perceptrons

Neural Computation, 1993
In this paper, we study the robustness of multilayer networks versus the destruction of neurons. We show that the classical backpropagation algorithm does not lead to optimal robustness and we propose a modified algorithm that improves this capability.
P. Kerlirzin, F. Vallet
openaire   +1 more source

Hyperconic Multilayer Perceptron

Neural Processing Letters, 2016
This paper introduces the design of the hyperconic multilayer perceptron (HC-MLP). Complex non-linear decision regions for classification purposes are generated by quadratic hyper-surfaces spawned by the hyperconic neurons in the hidden layer (for instance, spheres, ellipsoids, paraboloids, hyperboloids and degenerate conics).
Juan Pablo Serrano-Rubio, Arturo Hernández Aguirre, Rafael Herrera Guzmán   +2 more
openaire   +1 more source

Self-Organizing MultiLayer Perceptron

IEEE Transactions on Neural Networks, 2010
In this paper, we propose an extension of a self-organizing map called self-organizing multilayer perceptron (SOMLP) whose purpose is to achieve quantization of spaces of functions. Based on the use of multilayer perceptron networks, SOMLP comprises the unsupervised as well as supervised learning algorithms.
openaire   +3 more sources

On Langevin Updating in Multilayer Perceptrons

Neural Computation, 1994
The Langevin updating rule, in which noise is added to the weights during learning, is presented and shown to improve learning on problems with initially ill-conditioned Hessians. This is particularly important for multilayer perceptrons with many hidden layers, that often have ill-conditioned Hessians. In addition, Manhattan updating is shown to have
openaire   +2 more sources

On the decision regions of multilayer perceptrons

Proceedings of the IEEE, 1990
The capabilities of two-layer perceptrons are examined with respect to the geometric properties of the decision regions they are able to form. It is known that two-layer perceptrons can form decision regions which are nonconvex and even disconnected, though the extent of their capabilities in comparison to three-layer structures is not well understood.
Gavin J. Gibson, Colin F. N. Cowan
openaire   +1 more source

Multilayer perceptrons and data analysis

IEEE 1988 International Conference on Neural Networks, 1988
Results are presented which permit comparison of classification tasks of multilayer perceptrons with discriminant analysis. The results are illustrated with simulations of both approaches that demonstrate that multilayer perceptrons with nonlinear elements outperform discriminant analysis. >
Patrick Gallinari, Sylvie Thiria, Françoise Fogelman   +2 more
openaire   +1 more source

Dynamic sizing of multilayer perceptrons

Biological Cybernetics, 1994
This article proposes a stochastic method for determining the number of hidden nodes of a multilayer perceptron trained by a backpropagation algorithm. During the learning process, an auxiliary markovian algorithm controls the sizing of the hidden layers.
Bruno Apolloni, G. Ronchini
openaire   +1 more source

MMLD Inference of Multilayer Perceptrons

2013
A 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
openaire   +1 more source