Title :
Fast learning for multi-layer perceptrons using statistical techniques
Author :
Buhrke, Eric R. ; LoCicero, Joseph L.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
Abstract :
The authors describe a new learning algorithm for the multi-layer perceptron. The learning problem is stated formally as an optimization problem that is solved through a series of systematic approximations. The solution uses the moments of the training data to design the network. This procedure has several advantages, most importantly the reduction in training time. The algorithm is verified and compared to backpropagation. In a speech recognition experiment the total training time was reduced by more than 75% when compared to backpropagation
Keywords :
feedforward neural nets; learning (artificial intelligence); optimisation; speech recognition; statistical analysis; backpropagation; learning algorithm; learning problem; multi-layer perceptrons; optimization problem; speech recognition; statistical techniques; systematic approximations; training data moments; training time; Artificial neural networks; Backpropagation algorithms; Equations; Multi-layer neural network; Multilayer perceptrons; Network topology; Neural networks; Neurons; Speech recognition; Training data;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.225887
