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Current Result Document :
4
/ 4
ÀÌÀü°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
ÇнÀµÈ Áö½ÄÀÇ ºÐ¼®À» ÅëÇÑ ½Å°æ¸Á À籸¼º ¹æ¹ý
¿µ¹®Á¦¸ñ(English Title)
Restructuring a Feed-forward Neural Network Using Hidden Knowledge Analysis
ÀúÀÚ(Author)
±èÇöö
¿ø¹®¼ö·Ïó(Citation)
VOL 29 NO. 05 PP. 0289 ~ 0294 (2002. 06)
Çѱ۳»¿ë
(Korean Abstract)
´ÙÃþ½Å°æȸ·Î¸Á ±¸Á¶ÀÇ À籸¼ºÀº ȸ·Î¸ÁÀÇ ÀϹÝÈ ´É·ÂÀ̳ª È¿À²¼ºÀÇ °üÁ¡¿¡¼ Áß¿äÇÑ ¹®Á¦·Î ¿¬±¸µÇ¾î¿Ô´Ù. º» ³í¹®¿¡¼´Â ½Å°æȸ·Î¸Á¿¡ ÇнÀµÈ Àº´Ð Áö½ÄµéÀ» ÃßÃâÇÏ¿© Á¶ÇÕÇÔÀ¸·Î½á ½Å°æȸ·Î¸ÁÀÇ ±¸Á¶¸¦ À籸¼ºÇÏ´Â »õ·Î¿î ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. ¸ÕÀú, °¢ ³ëµåº°·Î ÇнÀµÈ ´ëÇ¥ÀûÀÎ Áö¿ª ±ÔÄ¢À» ÃßÃâÇÏ¿© °¢ ³ëµåÀÇ ºÒÇÊ¿äÇÑ ¿¬°á±¸Á¶µéÀ» Á¦°ÅÇÑ ÈÄ, À̵éÀÇ ³í¸®ÀûÀÎ Á¶ÇÕÀ» ÅëÇÏ¿© Áߺ¹ ¶Ç´Â »óÃæµÇ´Â ³ëµå¿Í ¿¬°á±¸Á¶¸¦ Á¦°ÅÇÑ´Ù. ÀÌ·¸°Ô ÇнÀµÈ Áö½ÄÀ» ºÐ¼®ÇÏ¿© ³ëµå¿Í ¿¬°á±¸Á¶¸¦ À籸¼ºÇÑ ½Å°æȸ·Î¸ÁÀº óÀ½ÀÇ ½Å°æȸ·Î¸Á¿¡ ºñÇÏ¿© ¿ùµîÈ÷ °¨¼ÒµÈ ±¸Á¶ º¹Àâµµ¸¦ °¡Áö¸ç ÀϹÝÀûÀ¸·Î ´õ ¿ì¼öÇÑ ÀϹÝÈ ´É·ÂÀ» °¡Áö°Ô µÊÀ» ½ÇÇè°á°ú·Î¼ Á¦½ÃÇÏ¿´´Ù.
¿µ¹®³»¿ë
(English Abstract)
It is known that restructuring of a feed-forward neural network affects generalization capability and efficiency of the network. In this paper, we introduce a new approach to restructure a neural network using abstraction of the hidden knowledge that the network has learned. This method involves extracting local rules from non-input nodes and aggregation of the rules into global rule base. The extracted local rules are used for pruning unnecessary connections of local nodes and the aggregation eliminates any possible redundancies and inconsistencies among local rule-based structures. Final network is generated by the global rule-based structure. Complexity of the final network is much reduced, compared to a fully-connected neural network and generalization capability is improved. Empirical results are also shown.
Å°¿öµå(Keyword)
Áö½Ä±â¹Ý ½Å°æ¸Á
±ÔÄ¢ÃßÃâ
½Å°æ¸Á Á¤Á¦
Know ledge-Based Neural Network
Rule Extraction
Neural Network Pruning
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