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ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 2 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
IDA
2008
Springer
15 years 4 months ago
Symbolic methodology for numeric data mining
Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectivene...
Boris Kovalerchuk, Evgenii Vityaev
JSW
2007
126views more  JSW 2007»
15 years 4 months ago
On Remote and Virtual Experiments in eLearning
— The science of physics is based on theories and models as well as experiments: the former structure relations and simplify reality to a degree such that predictions on physical...
Sabina Jeschke, Harald Scheel, Thomas Richter, Chr...
ICML
2003
IEEE
16 years 5 months ago
An Analysis of Rule Evaluation Metrics
In this paper we analyze the most popular evaluation metrics for separate-and-conquer rule learning algorithms. Our results show that all commonly used heuristics, including accur...
Johannes Fürnkranz, Peter A. Flach
AAAI
2006
15 years 5 months ago
Cross-Domain Knowledge Transfer Using Structured Representations
Previous work in knowledge transfer in machine learning has been restricted to tasks in a single domain. However, evidence from psychology and neuroscience suggests that humans ar...
Samarth Swarup, Sylvian R. Ray