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» Discriminative Learning of Max-Sum Classifiers
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BMCBI
2010
118views more  BMCBI 2010»
13 years 10 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
KES
2006
Springer
13 years 9 months ago
The Performance of LVQ Based Automatic Relevance Determination Applied to Spontaneous Biosignals
The issue of Automatic Relevance Determination (ARD) has attracted attention over the last decade for the sake of efficiency and accuracy of classifiers, and also to extract knowle...
Martin Golz, David Sommer
MICAI
2007
Springer
14 years 4 months ago
Weighted Instance-Based Learning Using Representative Intervals
Instance-based learning algorithms are widely used due to their capacity to approximate complex target functions; however, the performance of this kind of algorithms degrades signi...
Octavio Gómez, Eduardo F. Morales, Jes&uacu...
CVPR
2007
IEEE
14 years 12 months ago
Learning Visual Representations using Images with Captions
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
Ariadna Quattoni, Michael Collins, Trevor Darrell
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
15 years 5 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...