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» Discovering Classification from Data of Multiple Sources
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PERCOM
2010
ACM
13 years 6 months ago
Towards the adaptive integration of multiple context reasoners in pervasive computing environments
Abstract—The pervasive computing vision consists in realizing ubiquitous technologies to support the execution of people’s everyday tasks by proactively providing appropriate i...
Daniele Riboni, Linda Pareschi, Claudio Bettini
ICDM
2010
IEEE
193views Data Mining» more  ICDM 2010»
13 years 5 months ago
Supervised Link Prediction Using Multiple Sources
Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Facebook and Myspace. Most existing research approaches this pro...
Zhengdong Lu, Berkant Savas, Wei Tang, Inderjit S....
CORR
2010
Springer
208views Education» more  CORR 2010»
13 years 7 months ago
Discovering potential user browsing behaviors using custom-built apriori algorithm
Most of the organizations put information on the web because they want it to be seen by the world. Their goal is to have visitors come to the site, feel comfortable and stay a whi...
Sandeep Singh Rawat, Lakshmi Rajamani
CVPR
2010
IEEE
13 years 7 months ago
Boosting for transfer learning with multiple sources
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. Th...
Yi Yao, Gianfranco Doretto
BMCBI
2010
142views more  BMCBI 2010»
13 years 7 months ago
Classification of protein sequences by means of irredundant patterns
Background: The classification of protein sequences using string algorithms provides valuable insights for protein function prediction. Several methods, based on a variety of diff...
Matteo Comin, Davide Verzotto