IJON
2011
13 years 2 months ago
2011
We introduce a machine learning based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanosc...
GECCO
2011
Springer
13 years 2 months ago
2011
Springer
In continuous optimisation, surrogate models (SMs) are used when tackling real-world problems whose candidate solutions are expensive to evaluate. In previous work, we showed that...
SIAMSC
2011
13 years 6 months ago
2011
Abstract. Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can ...
SMI
2010
IEEE
13 years 9 months ago
2010
IEEE
—The level set method can implement a wide variety of shape modeling operations (e.g. offsetting, skeletonization, morphing, blending, smoothing, sharpening, embossing, denoising...
KBS
2002
13 years 11 months ago
2002
This paper presents a novel and interesting combination of wavelet techniques and eigenfaces to extract features for face recognition. Eigenfaces reduce the dimensions of face vec...
NPL
2000
13 years 11 months ago
2000
A Hybrid modeling approach, combining an analytical model with a radial basis function neural network is introduced in this paper. The modeling procedure is combined with genetic a...
TNN
2008
13 years 11 months ago
2008
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
TITB
2008
13 years 11 months ago
2008
Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their hig...
PR
2006
13 years 11 months ago
2006
Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor c...
NPL
2006
13 years 11 months ago
2006
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training...