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TKDE
2010
168views more  TKDE 2010»
15 years 5 months ago
Completely Lazy Learning
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...
TSP
2010
15 years 1 months ago
Double sparsity: learning sparse dictionaries for sparse signal approximation
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
Ron Rubinstein, Michael Zibulevsky, Michael Elad
CVPR
2009
IEEE
17 years 1 days ago
Learning Signs from Subtitles: A Weakly Supervised Approach to Sign Language Recognition
This paper introduces a fully-automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Bas...
Helen Cooper, Richard Bowden
BIOADIT
2004
Springer
16 years 21 hour ago
A Hardware Implementation of a Network of Functional Spiking Neurons with Hebbian Learning
Abstract. In this paper we present a functional model of a spiking neuron intended for hardware implementation. Some features of biological spiking neuabstracted, while preserving ...
Andres Upegui, Carlos Andrés Peña-Re...
ESANN
2008
15 years 8 months ago
Generalized matrix learning vector quantizer for the analysis of spectral data
The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in t...
Petra Schneider, Frank-Michael Schleif, Thomas Vil...