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» Simplifying mixture models through function approximation
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IJON
2006
131views more  IJON 2006»
13 years 7 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
TMI
2008
136views more  TMI 2008»
13 years 7 months ago
Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior
We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wave...
François G. Meyer, Xilin Shen
ICPR
2006
IEEE
14 years 8 months ago
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning
It is possible to broadly characterize two approaches to probabilistic modeling in terms of generative and discriminative methods. Provided with sufficient training data the discr...
B. Michael Kelm, Chris Pal, Andrew McCallum
TSP
2008
69views more  TSP 2008»
13 years 7 months ago
Stochastic Stability Analysis for the Constant-Modulus Algorithm
We derive an easy-to-compute approximate bound for the range of step-sizes for which the constant-modulus algorithm (CMA) will remain stable if initialized close to a minimum of t...
Victor H. Nascimento, M. T. M. Silva
VISUALIZATION
2005
IEEE
14 years 1 months ago
Rendering Tetrahedral Meshes with Higher-Order Attenuation Functions for Digital Radiograph Reconstruction
This paper presents a novel method for computing simulated x-ray images, or DRRs (digitally reconstructed radiographs), of tetrahedral meshes with higher-order attenuation functio...
Ofri Sadowsky, Jonathan D. Cohen, Russell H. Taylo...