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» On improving application utility prediction
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AAAI
2011
14 years 7 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»
16 years 9 days 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
IMC
2010
ACM
15 years 4 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
GLOBECOM
2007
IEEE
16 years 1 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
16 years 1 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...