Title :
Clustering unlabeled data with SOMs improves classification of labeled real-world data
Author :
Dara, Rozita ; Kremer, Stefan C. ; Stacey, Deborah A.
Author_Institution :
Dept. of Comput. & Inf. Sci., Guelph Univ., Ont., Canada
fDate :
6/24/1905 12:00:00 AM
Abstract :
We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled data, performance is improved over using only the labeled data. Results are presented for a number of popular real-world benchmark problems from domains other than text. This shows one way in which unlabeled data can be used to enhance supervised learning in a general-purpose neural network
Keywords :
learning (artificial intelligence); multilayer perceptrons; pattern classification; pattern clustering; self-organising feature maps; SOMs; classification; general-purpose neural network; labeled real-world data; multilayer perceptron; self organizing map; supervised learning; unlabeled data; Computational Intelligence Society; Costs; Humans; Information science; Labeling; Neural networks; Organizing; Supervised learning; System testing; Training data;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007489
