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142
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ICML
2008
IEEE
16 years 4 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
ICML
2005
IEEE
16 years 4 months ago
A brain computer interface with online feedback based on magnetoencephalography
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG)...
Bernhard Schölkopf, Hubert Preißl, J&uu...
157
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ISCIS
2005
Springer
15 years 9 months ago
Classification of Volatile Organic Compounds with Incremental SVMs and RBF Networks
Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization perfo...
Zeki Erdem, Robi Polikar, Nejat Yumusak, Fikret S....
154
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CEC
2005
IEEE
15 years 5 months ago
XCS with computed prediction in continuous multistep environments
We apply XCS with computed prediction (XCSF) to tackle multistep reinforcement learning problems involving continuous inputs. In essence we use XCSF as a method of generalized rein...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
GRAPHICSINTERFACE
2003
15 years 5 months ago
Learning from Games: HCI Design Innovations in Entertainment Software
Computer games are one of the most successful application domains in the history of interactive systems. This success has come despite the fact that games were ‘separated at bir...
Jeff Dyck, David Pinelle, Barry Brown, Carl Gutwin