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ICANN
2005
Springer
15 years 5 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...
ALMOB
2008
69views more  ALMOB 2008»
15 years 8 hour ago
Learning from positive examples when the negative class is undetermined- microRNA gene identification
Background: The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We an...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
15 years 4 months ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns
ICML
2000
IEEE
15 years 4 months ago
Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning
Cognitive modeling with neural networks unrealistically ignores the role of knowledge in learning by starting from random weights. It is likely that effective use of knowledge by ...
Thomas R. Shultz, François Rivest
SP
2010
IEEE
187views Security Privacy» more  SP 2010»
15 years 3 months ago
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Abstract—In network intrusion detection research, one popular strategy for finding attacks is monitoring a network’s activity for anomalies: deviations from profiles of norma...
Robin Sommer, Vern Paxson