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KDD
1999
ACM
199views Data Mining» more  KDD 1999»
13 years 12 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
NIPS
2008
13 years 9 months ago
Online Metric Learning and Fast Similarity Search
Metric learning algorithms can provide useful distance functions for a variety of domains, and recent work has shown good accuracy for problems where the learner can access all di...
Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kr...
ICALT
2009
IEEE
13 years 11 months ago
Conditioning Factors for Group Management in Blended Learning Scenarios
The management of groups is an essential task in collaborative learning scenarios. In a blended learning situation, in which online activities and face-to-face activities are comb...
Mar Pérez-Sanagustín, Davinia Hern&a...
SIGKDD
2008
138views more  SIGKDD 2008»
13 years 7 months ago
Learning preferences of new users in recommender systems: an information theoretic approach
Recommender systems are a nice tool to help nd items of interest from an overwhelming number of available items. Collaborative Filtering (CF), the best known technology for recomme...
Al Mamunur Rashid, George Karypis, John Riedl
IJCNN
2008
IEEE
14 years 2 months ago
On-line bagging Negative Correlation Learning
— Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning o...
Fernanda L. Minku, Xin Yao