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SIGMOD
2011
ACM
206views Database» more  SIGMOD 2011»
14 years 9 months ago
Sampling based algorithms for quantile computation in sensor networks
We study the problem of computing approximate quantiles in large-scale sensor networks communication-efficiently, a problem previously studied by Greenwald and Khana [12] and Shri...
Zengfeng Huang, Lu Wang, Ke Yi, Yunhao Liu
SENSYS
2004
ACM
16 years 6 days ago
Medians and beyond: new aggregation techniques for sensor networks
Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), makin...
Nisheeth Shrivastava, Chiranjeeb Buragohain, Divya...
NIPS
2007
15 years 8 months ago
The Tradeoffs of Large Scale Learning
This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for...
Léon Bottou, Olivier Bousquet
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
16 years 7 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...
ICML
2005
IEEE
16 years 7 months ago
Healing the relevance vector machine through augmentation
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
Carl Edward Rasmussen, Joaquin Quiñonero Ca...