Induction of Rules From Learning Examples By A Modified L1 Regression Method
碩士 === 國立交通大學 === 資訊管理研究所 === 84 === This thesis uses a modified L1 regression method to induce rules from large learning examples. The deficiencies of current learning methods (AQ and Neural Networks ) are : firstly , they can only fi...
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
1996
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| Online Access: | http://ndltd.ncl.edu.tw/handle/93103983917789239381 |
| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 84 === This thesis uses a modified L1 regression method to induce
rules from large learning examples. The deficiencies of current
learning methods (AQ and Neural Networks ) are : firstly , they
can only find local optimum ; secondly , they are not allowed
to add constraints. This thesis proposes a new method to deduce
optimal rules in reasonable computation time. Ananimal
classification instance verifies that the multiple layers
learning method is better than the single layer learning
method.
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