{'fraction_non_alphanumeric': 0.01757469244288225, 'fraction_numerical': 0.0, 'mean_word_length': 4.9375, '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}}
Improving on the Mixture of Experts Algorithm

Difficult non-linear problems can be mapped to a set of localised linear problems. A self-organising map (SOM) is used as a gating function to a localised mixture of experts classifier and is shown to find solutions equivalent to those learned by a multi-layer perceptron while retaining the simplicity and resilience of a single-layer perceptron. Modifications to the traditional softmax gate function and expert training function are shown to produce more accurate classifiers that tend not to over-fit to training data.