Neural network separation of temporal data

The main motivation for this research is three fold: a) temporal data classification is a challenging problem for pattern recognition tools and sophisticated tools are consistently sought to improve classifier performance on such type of data; b) traditional methods of treating temporal data often fail to adequately use temporal relationships between data points in their separation; and c) the development of neural tools based on new ideas capable of handling highly non-linear and noisy data will be of significant use in time-series, signal processing and speech applications. In this study we experiment with two types of data benchmarks: speech classification benchmark from NIST and 3D extension of the classical spiral benchmark from the Carnegie repository. It has been demonstrated in the past that ordinary neural network techniques for classification working on raw inputs of these benchmarks are inadequate. We propose a polygon method of temporal feature selection from time-dependent data and investigate neural network performance using a standard MLP architecture on such input