Iterative least squares development of discriminant functions for spectroscopic data analysis by pattern recognition

摘要

Chemical data can be automatically classified into useful chemical categories by computerized learning machines using adaptive pattern dichotomizers. Previous investigations have employed negative, error correction feedback procedures to train the two-class pattern categorizers. This paper reports a new method based on an iterative least squares procedure developed for obtaining the discriminant functions used by the pattern dichotomizers. The iterative least squares method is discussed in detail, and its application to the training of decision makers to classify low resolution mass spectra is demonstrated. It is shown that pattern dichotomizers trained with the method can obtain predictive abilities of 98% in classifying unknown low resolution mass spectra into useful chemical categories.