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CEAS
2007
Springer
13 years 11 months ago
Online Active Learning Methods for Fast Label-Efficient Spam Filtering
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustwo...
D. Sculley
PERCOM
2006
ACM
14 years 7 months ago
Context-Aware Resource Management in Multi-Inhabitant Smart Homes: A Nash H-Learning based Approach
A smart home aims at building intelligence automation with a goal to provide its inhabitants with maximum possible comfort, minimize the resource consumption and thus overall cost...
Nirmalya Roy, Abhishek Roy, Sajal K. Das
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
14 years 8 months ago
Effective multi-label active learning for text classification
Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical ...
Bishan Yang, Jian-Tao Sun, Tengjiao Wang, Zheng Ch...
IM
2007
13 years 9 months ago
Estimating End-to-End Performance by Collaborative Prediction with Active Sampling
— Accurately estimating end-to-end performance in distributed systems is essential both for monitoring compliance with service-level agreements (SLAs) and for performance optimiz...
Irina Rish, Gerald Tesauro
PERCOM
2007
ACM
14 years 7 months ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday