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ESANN
2003
15 years 8 months ago
Neural networks organizations to learn complex robotic functions
Abstract. This paper considers the general problem of function estimation with a modular approach of neural computing. We propose to use functionally independent subnetworks to lea...
Gilles Hermann, Patrice Wira, Jean-Philippe Urban
DAC
2004
ACM
16 years 8 months ago
A method to decompose multiple-output logic functions
This paper shows a method to decompose a given multipleoutput circuit into two circuits with intermediate outputs. We use a BDD for characteristic function (BDD for CF) to represe...
Tsutomu Sasao, Munehiro Matsuura
CIBCB
2005
IEEE
16 years 25 days ago
Functional Distances for Genes Based on GO Feature Maps and their Application to Clustering
— With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data, the need for a functiona...
Nora Speer, Holger Fröhlich, Christian Spieth...
AISC
2004
Springer
16 years 18 days ago
Solving Equations Involving Sequence Variables and Sequence Functions
Term equations involving individual and sequence variables and sequence function symbols are studied. Function symbols can have either fixed or flexible arity. A sequence variabl...
Temur Kutsia
IWANN
2009
Springer
15 years 11 months ago
Lower Bounds for Approximation of Some Classes of Lebesgue Measurable Functions by Sigmoidal Neural Networks
We propose a general method for estimating the distance between a compact subspace K of the space L1 ([0, 1]s ) of Lebesgue measurable functions defined on the hypercube [0, 1]s ,...
José Luis Montaña, Cruz E. Borges