Machine learning of inductive bias
Machine learning of inductive bias
Structure identification of fuzzy model
Fuzzy Sets and Systems
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition
Information Sciences: an International Journal - Special issue on advanced neuro-fuzzy techniques and their applications
Machine Learning - Special issue on inductive transfer
A survey of connectionist network reuse through transfer
Learning to learn
Learning to learn
Combining predictors: comparison of five meta machine learning methods
Information Sciences: an International Journal
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Extracting compact fuzzy rules based on adaptive data approximation using B-splines
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
A perspective view and survey of meta-learning
Artificial Intelligence Review
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Communicating neural networks in a multiagent system
Communicating neural networks in a multiagent system
Selective transfer of neural network task knowledge
Selective transfer of neural network task knowledge
Introduction to the Special Issue on Meta-Learning
Machine Learning
Information Sciences: an International Journal
A model of inductive bias learning
Journal of Artificial Intelligence Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
Bias learning, knowledge sharing
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
User preferences based software defect detection algorithms selection using MCDM
Information Sciences: an International Journal
Towards UCI+: A mindful repository design
Information Sciences: an International Journal
| Hi-index | 0.07 |
Common inductive learning strategies offer tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome the limitations of such base-learning approaches, a research trend explores the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This may lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. In this paper, we present a meta-learning framework called Mindful (Meta INDuctive neuro-FUzzy Learning) which is founded on the integration of connectionist paradigms and fuzzy knowledge management. Due to its peculiar organisation, Mindful can be exploited on different levels of application, being able to accumulate learning experience in cross-task contexts. This specific knowledge is gathered during the meta-learning activity and it is exploited to suggest parametrisation for future base-learning tasks. The evaluation of the Mindful system is detailed through an ensemble of experimental sessions involving both synthetic domains and real-world data.