{'fraction_non_alphanumeric': 0.012345679012345678, 'fraction_numerical': 0.0, 'mean_word_length': 5.540322580645161, '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}}
Training radial basis function classifiers with Gaussian kernel clustering and fuzzy decision technique

Radial basis function (RBF) neural networks have been used extensively in many applications for their simple architecture and fast learning. This paper principally discusses the training problem of RBF classifiers which can be used for classification. For RBF classifiers, how to correctly initialize the number of network hidden nodes and their parameters is very important. Genetic-based Gaussian kernel clustering method and fuzzy decision technique are explored to complete this work. Then the network is trained further with back propagation learning algorithm in order to attain optimal performance. Results from the typical experiments are used to illustrate the power and efficiency of the method.