On the design of supra-classifiers for knowledge reuse
| Autor: | Joydeep Ghosh, Kurt D. Bollacker |
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| Rok vydání: | 2002 |
| Předmět: |
Training set
Artificial neural network business.industry Computer science Bayesian probability Decision tree Reuse Machine learning computer.software_genre Task (project management) ComputingMethodologies_PATTERNRECOGNITION Function approximation Artificial intelligence Data mining business computer |
| Zdroj: | 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227). |
| DOI: | 10.1109/ijcnn.1998.685981 |
| Popis: | We (1997) have introduced a framework for the reuse of knowledge from previously trained classifiers to improve performance in a current, possibly related classification task. This framework requires the use of a supra-classifier, which makes a classification decision based on the outputs of a large number of previously trained diverse classifiers. We discuss the performance requirements of a good supra-classifier and introduce several possible supra-classifier architectures. We make performance comparisons of these architectures using public domain data sets for the problem of inadequate training data and compare their scalability in the number of simultaneously reused classifiers. |
| Databáze: | OpenAIRE |
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