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» Unbundling Active Functionality
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154
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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 4 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
139
Voted
MOBICOM
2004
ACM
15 years 9 months ago
Analyzing the spread of active worms over VANET
Interactive communications among nodes in Vehicular Ad Hoc Networks (VANET) and the safety-oriented nature of many VANET applications necessitate a robust security framework. An a...
Syed A. Khayam, Hayder Radha
160
Voted
CASES
2008
ACM
15 years 5 months ago
Active control and digital rights management of integrated circuit IP cores
We introduce the first approach that can actively control multiple hardware intellectual property (IP) cores used in an integrated circuit (IC). The IP rights owner(s) can remotel...
Yousra Alkabani, Farinaz Koushanfar
MMS
2006
15 years 3 months ago
View-invariant motion trajectory-based activity classification and recognition
Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams ...
Faisal I. Bashir, Ashfaq A. Khokhar, Dan Schonfeld
152
Voted
ISVC
2007
Springer
15 years 9 months ago
Boosting with Temporal Consistent Learners: An Application to Human Activity Recognition
We present a novel boosting algorithm where temporal consistency is addressed in a short-term way. Although temporal correlation of observed data may be an important cue for classi...
Pedro Canotilho Ribeiro, Plinio Moreno, José...