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ESANN
2006
14 years 11 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
83
Voted
INFOCOM
2010
IEEE
14 years 8 months ago
Compressive Oversampling for Robust Data Transmission in Sensor Networks
—Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensin...
Zainul Charbiwala, Supriyo Chakraborty, Sadaf Zahe...
INFOCOM
2008
IEEE
15 years 3 months ago
Swarming on Optimized Graphs for n-Way Broadcast
—In an n-way broadcast application each one of n overlay nodes wants to push its own distinct large data file to all other n-1 destinations as well as download their respective ...
Georgios Smaragdakis, Azer Bestavros, Nikolaos Lao...
71
Voted
CORR
2010
Springer
143views Education» more  CORR 2010»
14 years 9 months ago
Efficient and exact sampling of simple graphs with given arbitrary degree sequence
Uniform sampling from graphical realizations of a given degree sequence is a fundamental component in simulation-based measurements of network observables, with applications rangi...
Charo I. Del Genio, Hyunju Kim, Zoltán Toro...
87
Voted
ICIP
2008
IEEE
15 years 3 months ago
Image representation by compressed sensing
This paper addresses the image representation problem in visual sensor networks. We propose a new image representation scheme based on compressive sensing (CS) because compressive...
Bing Han, Feng Wu, Dapeng Wu