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» Machine Learning by Function Decomposition
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ICML
2008
IEEE
16 years 5 months ago
The asymptotics of semi-supervised learning in discriminative probabilistic models
Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a chall...
François Yvon, Nataliya Sokolovska, Olivier...
BMCBI
2008
100views more  BMCBI 2008»
15 years 4 months ago
High-precision high-coverage functional inference from integrated data sources
Background: Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of k...
Bolan Linghu, Evan S. Snitkin, Dustin T. Holloway,...
ICASSP
2009
IEEE
15 years 11 months ago
Functional estimation in Hilbert space for distributed learning in wireless sensor networks
In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new ...
Paul Honeine, Cédric Richard, José C...
188
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JCSS
2008
138views more  JCSS 2008»
15 years 4 months ago
Reducing mechanism design to algorithm design via machine learning
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a broad class of re...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
JIB
2006
220views more  JIB 2006»
15 years 4 months ago
An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions
Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from hi...
Fiona Browne, Haiying Wang, Huiru Zheng, Francisco...