Title :
A comparison of Eclectic learning and Stagger
Author :
Batsaihan, Jargalsaihan ; Barker, Cory
Author_Institution :
Dept. of Comput. Sci., Brigham Young Univ., Provo, UT, USA
Abstract :
This project compares two machine-learning methods, Stagger and Eclectic, on their classification correctness. Both systems were tested with real-world data sets previously used and tested in other machine learning and statistical literature. The Eclectic system performed better than Stagger on every data set
Keywords :
Boolean functions; generalisation (artificial intelligence); learning (artificial intelligence); learning systems; neural nets; pattern classification; Boolean function; Eclectic system; Stagger system; generalisation; machine-learning; neural nets; pattern classification; Computer networks; Computer science; Counting circuits; Impedance matching; Iris; Learning systems; Machine learning; Machine learning algorithms; Pattern matching; System testing;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831144
