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IJCAI
1989
13 years 6 months ago
Building Robust Learning Systems by Combining Induction and Optimization
Each concept description language and search strategy has an inherent inductive bias, a preference for some hypotheses over others. No single inductive bias performs optimally on ...
David K. Tcheng, Bruce L. Lambert, Stephen C. Y. L...
FUIN
2002
132views more  FUIN 2002»
13 years 4 months ago
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decisi...
Grzegorz Góra, Arkadiusz Wojna
RAID
1999
Springer
13 years 9 months ago
Combining Knowledge Discovery and Knowledge Engineering to Build IDSs
We have been developing a data mining (i.e., knowledge discovery) framework, MADAM ID, for Mining Audit Data for Automated Models for Intrusion Detection [LSM98, LSM99b, LSM99a]. ...
Wenke Lee, Salvatore J. Stolfo
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
13 years 10 months ago
Combining competent crossover and mutation operators: a probabilistic model building approach
This paper presents an approach to combine competent crossover and mutation operators via probabilistic model building. Both operators are based on the probabilistic model buildin...
Cláudio F. Lima, Kumara Sastry, David E. Go...
ICML
2006
IEEE
14 years 5 months ago
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...