An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Support feature machine for DNA microarray data
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Task management in advanced computational intelligence system
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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We present a novel approach to meta-learning, which is not just a ranking of methods, not just a strategy for building model committees, but an algorithm performing a search similar to what human experts do when analyzing data, solving full scope of data mining problems. The search through the space of possible solutions is driven by special mechanisms of machine generators based on meta-schemes. The approach facilitates using human experts knowledge to restrict the search space and gaining meta-knowledge in an automated manner. The conclusions help in further search and may also be passed to other meta-learners. All the functionality is included in our new general architecture for data mining, especially eligible for meta-learning tasks.