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» Machine Learning by Function Decomposition
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ICML
2008
IEEE
16 years 2 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 1 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,...
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ICASSP
2009
IEEE
15 years 8 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...
JCSS
2008
138views more  JCSS 2008»
15 years 1 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 1 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...