Structure identification of fuzzy model
Fuzzy Sets and Systems
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Learning to learn
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
A perspective view and survey of meta-learning
Artificial Intelligence Review
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Knowledge discovery by a neuro-fuzzy modeling framework
Fuzzy Sets and Systems
Meta-data: characterization of input features for meta-learning
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Meta-learning practices concern the dynamical search of the bias presiding over the behaviour of artificial learning systems. In this paper we present an original meta-learning framework, namely the Mindful (Meta INDuctive neuro-FUzzy Learning) system. Mindful is based on a neuro-fuzzy learning strategy providing for the inductive processes applicable both to ordinary base-level tasks and to more general cross-task applications. The peculiar organisation of the system allows a suitable meta-knowledge management, in order to carry on meta-learning investigations and to develop life-long learning strategies.