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ML
2000
ACM
154views Machine Learning» more  ML 2000»
15 years 1 months ago
Lazy Learning of Bayesian Rules
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
Zijian Zheng, Geoffrey I. Webb
ICANN
2005
Springer
15 years 7 months ago
Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...
ICALT
2006
IEEE
15 years 7 months ago
Adaptive e-Learning Methods and IMS Learning Design: An Integrated Approach
This position paper shows how several classical methods in adaptive learning can be addressed using IMS Learning Design. After a definition of four main questions to classify adap...
Daniel Burgos, Marcus Specht
KDD
2005
ACM
158views Data Mining» more  KDD 2005»
16 years 2 months ago
Adversarial learning
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on ad...
Daniel Lowd, Christopher Meek
ICML
2006
IEEE
16 years 2 months ago
Cost-sensitive learning with conditional Markov networks
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Prithviraj Sen, Lise Getoor