Abstract
Here is presented CAMLET that is a platform for automatic composition of inductive applications using ontologies that specify inductive learning methods. CAMLET constructs inductive applications using process and object ontologies. After instantiating, compiling and executing the basic design specification, CAMLET refines the specification based on the following refinement strategies: crossover of control structures, random generation and process replacement by heuristic. Using fourteen different data sets form the UCI repository of ML databases and and the database on meningoencephalitis with human expert’s evaluation, experimental results have shown us that CAMLET supports a user in constructing inductive applications with better competence.
Keywords
- Random Generation
- Back Propagation Neural Network
- Conceptual Hierarchy
- Process Ontology
- Machine Learning System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1999 Springer-Verlag Berlin Heidelberg
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Suyama, A., Negishi, N., Yamaguchi, T. (1999). Design and Evaluation of an Environment to Automate the Construction of Inductive Applications. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_10
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DOI: https://doi.org/10.1007/3-540-46846-3_10
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