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
Introduction to the Special Issue on Meta-Learning
Machine Learning
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
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
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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 results of an ensemble of experimental sessions are detailed, proving the appropriateness of the system in managing meta-level contexts of learning.