Title :
Signal melding-the construction of training vectors for classifying data series
Author_Institution :
Dept. of Comput. Sci., Tasmania Univ., Hobart, Tas., Australia
Abstract :
This paper presents a beneficial technique for dynamically generating intermediate target output(s) for use in the training of classifiers on sequentially presented data series. The effects on accuracy and generalisability are examined by pitting the new technique, signal melding, against several standard back propagation through time techniques and a feed forward technique, cascade correlation
Keywords :
learning (artificial intelligence); neural nets; pattern classification; signal processing; data series classification; generalisability; intermediate target output dynamic generation; sequentially presented data series; signal melding; training vector construction; Artificial neural networks; Australia; Computer networks; Computer science; Feeds; Runtime; Signal generators; Signal processing;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487833