DocumentCode :
1809945
Title :
The learning behavior of single neuron classifiers on linearly separable or nonseparable input
Author :
Basu, Mitra ; Ho, Tin Kam
Author_Institution :
Dept. of Electr. Eng., City Univ. of New York, NY, USA
Volume :
2
fYear :
1999
fDate :
36342
Firstpage :
1259
Abstract :
Determining linear separability is an important way of understanding structures present in data. We explore the behavior of several classical descent procedures for determining linear separability and training linear classifiers in the presence of linearly nonseparable input. We compare the adaptive procedures to linear programming methods using many pairwise discrimination problems from a public database. We found that the adaptive procedures have serious implementation problems which make them less preferable than linear programming
Keywords :
learning (artificial intelligence); linear programming; neural nets; pattern classification; learning behavior; linear programming; linear separability; pairwise discrimination; pattern classification; single neuron adaptive classifiers; Cities and towns; Databases; Ear; Educational institutions; Equations; Geometry; Linear programming; Neurons; Space technology; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.831142
Filename :
831142
Link To Document :
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