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» Mobile learning: A framework and evaluation
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JMLR
2010
121views more  JMLR 2010»
13 years 3 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
FUZZIEEE
2007
IEEE
14 years 3 months ago
Soft Target Based Obstacle Avoidance for Car-like Mobile Robot in Dynamic Environment
— The real time flexible operation of a car-like mobile robot with nonholonomic constraints in dynamic environment is still a very challenging problem. The difficulty lies in t...
Yougen Chen, Seiji Yasunobu
AROBOTS
2011
13 years 4 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
EUROSYS
2006
ACM
14 years 6 months ago
URICA: Usage-awaRe Interactive Content Adaptation for mobile devices
Usage-awaRe Interactive Content Adaptation (URICA) is an automatic technique that adapts content for display on mobile devices based on usage semantics. URICA allows users who are...
Iqbal Mohomed, Jim Chengming Cai, Eyal de Lara
PKDD
2005
Springer
164views Data Mining» more  PKDD 2005»
14 years 2 months ago
Clustering and Prediction of Mobile User Routes from Cellular Data
Location-awareness and prediction of future locations is an important problem in pervasive and mobile computing. In cellular systems (e.g., GSM) the serving cell is easily availabl...
Kari Laasonen