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COLT
1992
Springer
15 years 10 months ago
Dominating Distributions and Learnability
We consider PAC-learning where the distribution is known to the student. The problem addressed here is characterizing when learnability with respect to distribution D1 implies lea...
Gyora M. Benedek, Alon Itai
LWA
2007
15 years 8 months ago
State of the Art of Adaptivity in E-Learning Platforms
Adaptivity has been an important research topic during the past two decades, especially in the field of e-learning. This paper deals with the question of whether and to what exte...
David Hauger, Mirkam Köck
ML
2000
ACM
15 years 6 months ago
Randomizing Outputs to Increase Prediction Accuracy
Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, grow predictors on these perturbed training sets and combine them. A que...
Leo Breiman
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 7 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
DGO
2008
126views Education» more  DGO 2008»
15 years 8 months ago
Active learning for e-rulemaking: public comment categorization
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
Stephen Purpura, Claire Cardie, Jesse Simons