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KDD
2004
ACM
117views Data Mining» more  KDD 2004»
16 years 3 months ago
Regularized multi--task learning
Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks...
Theodoros Evgeniou, Massimiliano Pontil
126
Voted
SAC
2006
ACM
15 years 9 months ago
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security co...
Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya
115
Voted
ECTEL
2007
Springer
15 years 9 months ago
Applying Sensemaking in a Mobile Learning Scenario
In this work, a new type of collaborative learning activity is proposed in order to enable students to explore and understand information in highly mobile situations. We call this ...
Gustavo Zurita, Nelson Baloian, Pedro Antunes, Fel...
CIKM
2010
Springer
15 years 1 months ago
Partial drift detection using a rule induction framework
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...
Damon Sotoudeh, Aijun An
PKDD
2010
Springer
184views Data Mining» more  PKDD 2010»
15 years 1 months ago
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Yuyang Wang, Roni Khardon, Pavlos Protopapas