Probabilistic spectral feature extraction technique for neural networks
Young R. Yoon, Okan K. Ersoy · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
Artificial neural net models have been studied for many years in the hope of achieving human- like performance in the fields of speech, image recognition and pattern recognition. For high performance and for controlling the size of the network, the input information must be preprocessed before being fed into the neural network. In this paper, a probabilistic spectral feature extraction technique (PSFET) with multiview spectral representations and its applications are described. During training and testing, the PSFET allows efficient extraction of useful information in addition to generating an input vector size for best classification performance by the following neural network. Experimental results indicate that the performance of the neural network increases in classification accuracy when PSFET is used at the input. The network also generalizes better.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.