Induction of Rules From Learning Examples By A Modified L1 Regression Method

• Main Authors: Wang, Ying-Jen, 王櫻珍
• Other Authors: Han-Lin Li
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/93103983917789239381

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.