Knowledge of extraction from trained neural network by using decision tree
Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black bo...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18263/ http://umpir.ump.edu.my/id/eprint/18263/ http://umpir.ump.edu.my/id/eprint/18263/1/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree.pdf http://umpir.ump.edu.my/id/eprint/18263/2/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree%201.pdf |
Internet
http://umpir.ump.edu.my/id/eprint/18263/http://umpir.ump.edu.my/id/eprint/18263/
http://umpir.ump.edu.my/id/eprint/18263/1/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree.pdf
http://umpir.ump.edu.my/id/eprint/18263/2/Knowledge%20of%20Extraction%20from%20Trained%20Neural%20Network%20by%20Using%20Decision%20Tree%201.pdf