{'fraction_non_alphanumeric': 0.012039127163280662, 'fraction_numerical': 0.0, 'mean_word_length': 5.963350785340314, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Pattern Category Assignment by Neural Networks and Nearest Neighbors Rules: A Synopsis and A Characterization

The purpose of this paper is two-fold: to give a synoptic description of favored neural networks and to characterize the potency of these neural networks as pattern classifiers, against the background of the familiar nearest neighbors classification. We limit the study to those neural network structures most commonly used for pattern classification: the multilayer perceptron, the Kohonen associative memory, and the Carpenter–Grossberg clustering network, for which we give a tutorial description with the aim of making the driving concepts apparent. The nearest neighbors rule is presented with improved nearest neighbor search and reference data sample pruning. To gain some familiarity with the classifiers, we expound the sequence of computations implicated in pattern category assignment by each classifier. A characterization of the classifiers is drawn from observed and expected properties and from experiments in automatic target recognition and optical character recognition as summarized in comparative tables of performance. This characterization supports the suggestion that nearest neighbors classification always be considered before endorsing alternative pattern classifiers such as neural networks.