Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Meta-learning with Machine Generators and Complexity Controlled Exploration
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
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Computational intelligence (CI) comes up with more and more sophisticated, hierarchical learning machines. Running advanced techniques, including meta-learning, requires general data mining systems, capable of efficient management of very complex machines. Requirements for running complex learning tasks, within such systems, are significantly different than those of running processes by operating systems. We address major requirements that should be met by CI systems and present corresponding solutions tested and implemented in our system. The main focus are the aspects of task spooling and multitasking.