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142
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JMLR
2010
192views more  JMLR 2010»
14 years 10 months ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
133
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IWANN
1997
Springer
15 years 7 months ago
The Pattern Extraction Architecture: A Connectionist Alternative to the Von Neumann Architecture
A detailed connectionist architecture is described which is capable of relating psychological behavior to the functioning of neurons and neurochemicals. The need to be able to bui...
L. Andrew Coward
143
Voted
DMIN
2006
113views Data Mining» more  DMIN 2006»
15 years 5 months ago
Back-propagation DEA
Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with...
Ali Emrouznejad
TSMC
1998
91views more  TSMC 1998»
15 years 3 months ago
Toward the border between neural and Markovian paradigms
— A new tendency in the design of modern signal processing methods is the creation of hybrid algorithms. This paper gives an overview of different signal processing algorithms si...
Piotr Wilinski, Basel Solaiman, A. Hillion, W. Cza...
148
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ESANN
2006
15 years 5 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...