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
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dynamic Automatic Model Selection
Dynamic Automatic Model Selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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In theory, learning is not possible over all tasks in general. In practice, the tasks for which learning is desired exhibit significant regularity, which makes learning practical. For the most effective learning, it is valuable to understand the nature of this regularity and how it manifests in the tasks where learning is applied. This research presents the DICES distance metric for finding similarity between learning tasks. With this distance metric, a collection of learning tasks can be given a distance matrix. This distance matrix can be used for visualizing the relationships between learning tasks and searching through task space for tasks which are similar in nature. Examples of task visualization are given, and other possible applications of this tool are touched upon. Such applications include learning algorithm selection, transfer learning, and analysis of empirical results.