Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Advanced ontology management system for personalised e-Learning
Knowledge-Based Systems
A computational model for developing semantic web-based educational systems
Knowledge-Based Systems
Context, situations, and design agents
Knowledge-Based Systems
Validated decision trees versus collective decisions
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Properties and structure of fast text search engine in context of semantic image analysis
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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There are many knowledge-based data mining frameworks and it is common to think that new ones cannot come up with anything new. This article refutes such claims. We propose a sophisticated unification mechanism and two-tier machine cache system aimed at saving time and memory. No machine is run twice. Instead, machines are reused wherever they are repeatedly requested (regardless of request context). We also present an exceptional task spooler. Its unique design facilitates efficient automated management of large numbers of tasks with natural adjustment to available computational resources. Dedicated task scheduler cooperates with machine unification mechanism to save time and space. The solutions are possible thanks to very general and universal design of machine, configuration, machine context, unique machine life cycle, machine information exchange, configuration templates and other necessary concepts. Results gained by machines are stored in a uniform way, facilitating easy results exploration and collection by means of a special query system and versatile analysis with series transformations. No knowledge about internals of particular machines is necessary to extensively explore the results. The ideas presented here, have been implemented and verified inside Intemi framework for data mining and meta-learning tasks. They are general engine-level mechanisms that may be fruitful in all aspects of data analysis, all applications of knowledge-based data mining, computational intelligence, machine learning or neural networks methods.