Title :
Explicit versus implicit set-covering for supervised learning
Author :
Kowalski, Stephen V. ; Moldovan, Dan
Author_Institution :
Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
It has been shown that implicit covering algorithms are effective for learning concepts from preclassified training examples. In this paper, we show that by making these covering algorithms explicit, concepts with lower error rates can be learned. Experimental results are reported for three real domains
Keywords :
errors; learning (artificial intelligence); set theory; concept learning; error rates; explicit set covering; implicit set covering; learning concepts; preclassified training examples; supervised learning; Artificial intelligence; Computer science; Error analysis; Humans; Supervised learning; Testing;
Conference_Titel :
Tools with Artificial Intelligence, 1994. Proceedings., Sixth International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-8186-6785-0
DOI :
10.1109/TAI.1994.346425
