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» Mobile learning: A framework and evaluation
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IDA
2003
Springer
14 years 2 months ago
A Semi-supervised Method for Learning the Structure of Robot Environment Interactions
For a mobile robot to act autonomously, it must be able to construct a model of its interaction with the environment. Oates et al. developed an unsupervised learning method that pr...
Axel Großmann, Matthias Wendt, Jeremy Wyatt
PVLDB
2010
133views more  PVLDB 2010»
13 years 7 months ago
RoadTrack: Scaling Location Updates for Mobile Clients on Road Networks with Query Awareness
Mobile commerce and location based services (LBS) are some of the fastest growing IT industries in the last five years. Location update of mobile clients is a fundamental capabil...
Péter Pesti, Ling Liu, Bhuvan Bamba, Arun I...
BMCBI
2010
118views more  BMCBI 2010»
13 years 9 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
VTC
2010
IEEE
102views Communications» more  VTC 2010»
13 years 7 months ago
Trace-Based Evaluation of Rate Adaptation Schemes in Vehicular Environments
Abstract—There has been a variety of rate adaptation solutions proposed for both indoor and mobile scenarios. However, dynamic channel changing conditions (e.g., temporal channel...
Kevin C. Lee, Juan M. Navarro, Tin Y. Chong, Uichi...
NETWORKING
2011
12 years 12 months ago
Epidemic Spread in Mobile Ad Hoc Networks: Determining the Tipping Point
Short-range, point-to-point communications for mobile users enjoy increasing popularity, particularly with the rise in Bluetooth-equipped mobile devices. Unfortunately, virus write...
Nicholas Valler, B. Aditya Prakash, Hanghang Tong,...