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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 8 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
SENSYS
2006
ACM
14 years 1 months ago
Funneling-MAC: a localized, sink-oriented MAC for boosting fidelity in sensor networks
Sensor networks exhibit a unique funneling effect which is a product of the distinctive many-to-one, hop-by-hop traffic pattern found in sensor networks, and results in a signific...
Gahng-Seop Ahn, Se Gi Hong, Emiliano Miluzzo, Andr...
CIKM
2010
Springer
13 years 5 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan
EUROGP
2005
Springer
115views Optimization» more  EUROGP 2005»
14 years 1 months ago
Genetic Programming in Wireless Sensor Networks
Abstract. Wireless sensor networks (WSNs) are medium scale manifestations of a paintable or amorphous computing paradigm. WSNs are becoming increasingly important as they attain gr...
Derek M. Johnson, Ankur Teredesai, Robert T. Salta...
INFOCOM
1999
IEEE
13 years 12 months ago
Scalable Flow Control for Multicast ABR Services
We propose a flow-control scheme for multicast ABR services in ATM networks. At the heart of the proposed scheme is an optimal secondorder rate control algorithm, called the -contr...
Xi Zhang, Kang G. Shin, Debanjan Saha, Dilip D. Ka...