Knowledge reuse in multiple classifier systems
| Autor: | Kurt D. Bollacker, Joydeep Ghosh |
|---|---|
| Rok vydání: | 1997 |
| Předmět: |
Training set
business.industry Computer science Mutual information Reuse computer.software_genre Machine learning Task (project management) Random subspace method Artificial Intelligence Signal Processing Relevance (information retrieval) Computer Vision and Pattern Recognition Artificial intelligence Data mining business computer Knowledge transfer Software |
| Zdroj: | Pattern Recognition Letters. 18:1385-1390 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/s0167-8655(97)00087-1 |
| Popis: | We introduce a framework for the reuse of knowledge from previously trained classifiers to improve performance in a current and possibly related classification task. The approach used is flexible in the type and relevance of reused classifiers and is also scalable. Experiments on public domain data sets demonstrate the usefulness of this approach when one is faced with very few training examples or very noisy training data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect