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» Learning Generative Models with the Up-Propagation Algorithm
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TIP
2010
145views more  TIP 2010»
14 years 4 months ago
Joint Manifolds for Data Fusion
The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage po...
Mark A. Davenport, Chinmay Hegde, Marco F. Duarte,...
NIPS
1996
14 years 11 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
ML
2000
ACM
124views Machine Learning» more  ML 2000»
14 years 9 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
CN
2007
149views more  CN 2007»
14 years 9 months ago
Adaptive congestion protocol: A congestion control protocol with learning capability
There is strong evidence that the current implementation of TCP will perform poorly in future high speed networks. To address this problem many congestion control protocols have b...
Marios Lestas, Andreas Pitsillides, Petros A. Ioan...
BMCBI
2007
133views more  BMCBI 2007»
14 years 9 months ago
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. ...
Ivan G. Costa, Roland Krause, Lennart Opitz, Alexa...