Abstract

Pattern recognition is very important for many aspects of data analysis and robotic control. Three pattern recognition techniques were examined-<i>k</i>-Nearest Neighbors, Bayesian analysis, and the C4.5 inductive learning algorithm. Their abilities to classify 71 different reference materials were compared. Each training and test example consisted of 79 different elemental concentrations. Different data sets were generated with relative standard deviations of 1, 3, 5, 10, 30, 100, and 500%. Each data set consisted of 2000 examples. These sets were used in both the training stages and in the test stages. It was found that C4.5's inductive learning algorithm had a higher classification accuracy than either Bayesian or <i>k</i>-Nearest Neighbors techniques, especially when large amounts of noise were present in the systems.

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