Title :
Efficient training techniques for classification with vast input space
Author :
Saad, E.W. ; Choi, J.J. ; Vian, J.L. ; Wunsch, D.C.
Author_Institution :
Dept. of Electr. Eng., Texas Tech. Univ., Lubbock, TX, USA
Abstract :
Strategies to efficiently train a neural network for an aerospace problem with a large multidimensional input space are developed and demonstrated. The neural network provides classification for over 100,000,000 data points. A query-based strategy is used that initiates training using a small input set, and then augments the set in multiple stages to include important data around the network decision boundary. Neural network inversion and oracle query are used to generate the additional data, jitter is added to the query data to improve the results, and an extended Kalman filter algorithm is used for training. A causality index is discussed as a means to reduce the dimensionality of the problem based on the relative importance of the inputs
Keywords :
Kalman filters; computational complexity; filtering theory; learning (artificial intelligence); neural nets; pattern classification; aerospace problem; causality index; classification; dimensionality reduction; efficient training techniques; extended Kalman filter algorithm; jitter; multidimensional input space; network decision boundary; neural network; neural network inversion; oracle query; query-based strategy; Aerospace safety; Airplanes; Error correction; Feeds; Jitter; Neural networks; Neurons; Predictive models; Real time systems; Software safety;
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.1999.831156
