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» Learning Rule Representations from Boolean Data
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ICML
2009
IEEE
15 years 7 months ago
Rule learning with monotonicity constraints
In classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing ...
Wojciech Kotlowski, Roman Slowinski
CN
2007
106views more  CN 2007»
15 years 15 days ago
Learning DFA representations of HTTP for protecting web applications
Intrusion detection is a key technology for self-healing systems designed to prevent or manage damage caused by security threats. Protecting web server-based applications using in...
Kenneth L. Ingham, Anil Somayaji, John Burge, Step...
129
Voted
IEAAIE
2010
Springer
14 years 10 months ago
Web Usage Mining for Improving Students Performance in Learning Management Systems
An innovative technique based on multi-objective grammar guided genetic programming (MOG3P-MI) is proposed to detect the most relevant activities that a student needs to pass a cou...
Amelia Zafra, Sebastián Ventura
91
Voted
UAI
1998
15 years 1 months ago
Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Nir Friedman, Kevin P. Murphy, Stuart J. Russell
JMLR
2008
117views more  JMLR 2008»
15 years 14 days ago
Closed Sets for Labeled Data
Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with ...
Gemma C. Garriga, Petra Kralj, Nada Lavrac