Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
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
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
Predicting relative performance of classifiers from samples
ICML '05 Proceedings of the 22nd international conference on Machine learning
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Hi-index | 0.00 |
Meta-Learning has been used to select algorithms based on the features of the problems being tackled. Each training example in this context, i.e. each meta-example, stores the features of a given problem and the performance information obtained by the candidate algorithms in the problem. The construction of a set of meta-examples may be costly, since the algorithms performance is usually defined through an empirical evaluation on the problem at hand. In this context, we proposed the use of Active Learning to select only the relevant problems for metaexample generation. Hence, the need for empirical evaluations of the candidate algorithms is reduced. Experiments were performed using the classification uncertainty of the k-NN algorithm as the criteria for active selection of problems. A significant gain in performance was yielded by using the Active Learning method.