Author: Cho, Yeun-Jin; Kim, Hyeoncheol
Title: Cleavage Site Analysis Using Rule Extraction from Neural Networks Cord-id: gc9hevy5 Document date: 2005_1_1
ID: gc9hevy5
Snippet: In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown.
Document: In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown.
Search related documents:
Co phrase search for related documents- accuracy high and machine learn: 1
- accuracy low and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9
Co phrase search for related documents, hyperlinks ordered by date