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CEAS
2007
Springer
15 years 10 months ago
Online Active Learning Methods for Fast Label-Efficient Spam Filtering
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustwo...
D. Sculley
KDD
2010
ACM
224views Data Mining» more  KDD 2010»
15 years 10 months ago
Multi-label learning by exploiting label dependency
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (ex...
Min-Ling Zhang, Kun Zhang
ECML
2006
Springer
15 years 10 months ago
Bayesian Active Learning for Sensitivity Analysis
Abstract. Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore th...
Tobias Pfingsten
ICALT
2009
IEEE
15 years 9 months ago
Using Students' Devices and a No-to-Low Cost Online Tool to Support Interactive Experiential mLearning
The rapid evolution and ubiquitous use of mobile devices is an historical opportunity to improve experiential interactivity in education practices to support “deep” learning. ...
Andrew Litchfield, Ryszard Raban, Laurel Evelyn Dy...
IADIS
2008
15 years 7 months ago
e-Learning Courseware Effort Estimate Model
This paper presents an improved software estimation model, which uses to estimate developing effort of e-Learning's contents. This model is called the e-Learning courseware E...
Vajirasak Vanijja, Athitha Chokananratana