Title :
Classification accuracy improvement of neural network classifiers by using unlabeled data
Author :
Fardanesh, M.T. ; Ersoy, Okan K.
Author_Institution :
Dept. of Commun. Sci. & Technol., California State Univ., Monterey Bay, CA, USA
fDate :
5/1/1998 12:00:00 AM
Abstract :
Classification accuracy improvement of neural network classifiers using unlabeled testing data is presented. In order to increase the classification accuracy without increasing the number of training data, the network makes use of testing data along with training data for learning. It is shown that including the unlabled samples from underrepresented classes in the training set improves the classification accuracy of some of the classes during supervised-unsupervised learning
Keywords :
feedforward neural nets; geophysical signal processing; geophysical techniques; geophysics computing; image classification; neural nets; remote sensing; accuracy improvement; geophysical measurement technique; image classification; image processing; land surface; neural net; neural network classifier; remote sensing; supervised learning; terrain mapping; training; underrepresented class; unlabeled data; unlabelled data; unsupervised learning; Artificial neural networks; High-resolution imaging; Image resolution; Neural networks; Optical imaging; Remote sensing; Space technology; Spatial resolution; Testing; Training data;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
