COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
A perspective view and survey of meta-learning
Artificial Intelligence Review
Combining Classifiers with Meta Decision Trees
Machine Learning
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Introduction to the Special Issue on Meta-Learning
Machine Learning
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
A modal symbolic classifier for selecting time series models
Pattern Recognition Letters
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Predicting relative performance of classifiers from samples
ICML '05 Proceedings of the 22nd international conference on Machine learning
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Active Selection of Training Examples for Meta-Learning
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Active learning with multiple views
Journal of Artificial Intelligence Research
Active learning to support the generation of meta-examples
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Combining meta-learning and active selection of datasetoids for algorithm selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Uncertainty sampling-based active selection of datasetoids for meta-learning
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
An automatic method for construction of ensembles to time series prediction
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
Meta-Learning has been successfully applied to acquire knowledge used to support the selection of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) is related to a learning problem and stores the experience obtained in the empirical evaluation of a set of candidate algorithms when applied to the problem. The generation of a good set of meta-examples can be a costly process depending for instance on the number of available learning problems and the complexity of the candidate algorithms. In this work, we proposed the Active Meta-Learning, in which Active Learning techniques are used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation. In an implemented prototype, we evaluated the use of two different Active Learning techniques applied in two different Meta-Learning tasks. The performed experiments revealed a significant gain in the Meta-Learning performance when the active techniques were used to support the meta-example generation.