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» On improving application utility prediction
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AAAI
2011
14 years 1 months ago
End-User Feature Labeling via Locally Weighted Logistic Regression
Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the lear...
Weng-Keen Wong, Ian Oberst, Shubhomoy Das, Travis ...
HICSS
2003
IEEE
88views Biometrics» more  HICSS 2003»
15 years 7 months ago
Expanding Citizen Access and Public Official Accountability through Knowledge Creation Technology: One Recent Development in e-D
The authors describe an addition to the conversation regarding enhanced democracy through technologicallyassisted means (e-Democracy) focusing on enhancing and expanding the typic...
Michael A. Shires, Murray S. Craig
132
Voted
IMC
2010
ACM
14 years 11 months ago
Network traffic characteristics of data centers in the wild
Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of current data centers...
Theophilus Benson, Aditya Akella, David A. Maltz
114
Voted
GLOBECOM
2007
IEEE
15 years 8 months ago
Aggregated Bloom Filters for Intrusion Detection and Prevention Hardware
—Bloom Filters (BFs) are fundamental building blocks in various network security applications, where packets from high-speed links are processed using state-of-the-art hardwareba...
N. Sertac Artan, Kaustubh Sinkar, Jalpa Patel, H. ...
CIKM
2009
Springer
15 years 8 months ago
Combining labeled and unlabeled data with word-class distribution learning
We describe a novel simple and highly scalable semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it the task of information extraction (IE) by utili...
Yanjun Qi, Ronan Collobert, Pavel Kuksa, Koray Kav...