Results 231 to 240 of about 21,160 (284)
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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
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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
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Robustness in Multilayer Perceptrons
Neural Computation, 1993In 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
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Hyperconic Multilayer Perceptron
Neural Processing Letters, 2016This 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 +2 more
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Self-Organizing MultiLayer Perceptron
IEEE Transactions on Neural Networks, 2010In 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.
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On Langevin Updating in Multilayer Perceptrons
Neural Computation, 1994The 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
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On the decision regions of multilayer perceptrons
Proceedings of the IEEE, 1990The 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
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Exponential-weight multilayer perceptron
2017 International Joint Conference on Neural Networks (IJCNN), 2017Analog integrated circuits may increase the neuromorphic network performance dramatically, leaving far behind their digital and biological counterparts, while approaching the energy efficiency of the brain. The key component of the most advanced analog circuit implementations is a nanodevice with adjustable conductance — essentially an analog ...
Farnood Merrikh-Bayat +2 more
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Multilayer perceptrons and data analysis
IEEE 1988 International Conference on Neural Networks, 1988Results 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 +2 more
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Multilayer perceptron for nonlinear programming
Computers & Operations Research, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jaques Reifman, Earl E. Feldman
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Dynamic sizing of multilayer perceptrons
Biological Cybernetics, 1994This 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
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