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» Class Noise Mitigation Through Instance Weighting
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ECML
2007
Springer
13 years 11 months ago
Class Noise Mitigation Through Instance Weighting
We describe a novel framework for class noise mitigation that assigns a vector of class membership probabilities to each training instance, and uses the confidence on the current ...
Umaa Rebbapragada, Carla E. Brodley
MICAI
2007
Springer
13 years 11 months ago
Weighted Instance-Based Learning Using Representative Intervals
Instance-based learning algorithms are widely used due to their capacity to approximate complex target functions; however, the performance of this kind of algorithms degrades signi...
Octavio Gómez, Eduardo F. Morales, Jes&uacu...
VTC
2008
IEEE
160views Communications» more  VTC 2008»
13 years 11 months ago
Reducing Feedback Requirements of the Multiple Weight Opportunistic Beamforming Scheme via Selective Multiuser Diversity
—Opportunistic beamforming (OB) relies on the transmission of Channel State Information (CSI) in the form of instantaneous Signal to Noise Ratio (SNR) from Mobile Stations (MSs) ...
Marios Nicolaou, Angela Doufexi, Simon Armour
JSAC
2008
136views more  JSAC 2008»
13 years 5 months ago
Relay-Assisted Decorrelating Multiuser Detector (RAD-MUD) for Cooperative CDMA Networks
Abstract-- In this paper, we examine the uplink of a cooperative CDMA network, where users cooperate by relaying each other's messages to the base station. When spreading wave...
Wan-Jen Huang, Yao-Win Peter Hong, C. C. Jay Kuo
ICIP
2009
IEEE
13 years 3 months ago
New image processing challenges for jointly designed electro-optical imaging systems
Still-image processing algorithms are tailored to and depend crucially upon the properties of the class of images to which they are applied, for instance natural images in consumer...
M. Dirk Robinson, David G. Stork