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A Comparative study of Artificial Neural Networks Using Reinforcement learning and Multidimensional Bayesian Classification Using Parzen Density Estimation for Identification of GC-EIMS Spectra of Partially Methylated Alditol Acetates
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Abstract: This study reports the development of a pattern recognition search engine for
a World Wide Web-based database of gas chromatography-electron impact mass
spectra (GC-EIMS) of partially methylated Alditol Acetates (PMAAs). Here, we
also report comparative results for two pattern recognition techniques that
were employed for this study. The first technique is a statistical technique
using Bayesian classifiers and Parzen density estimators. The second technique
involves an artificial neural network module t ...
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