{'fraction_non_alphanumeric': 0.010144927536231883, 'fraction_numerical': 0.0, 'mean_word_length': 5.457943925233645, '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}}
Logistic regression and the Boltzmann machine

A derivation of the learning algorithm for the Boltzmann machine is presented. It uses a statistical tool called logistic regression, in which the connection strengths in the Boltzmann machine correspond to the parameters of the logistic model. The use of maximum-likelihood estimates for the parameters leads to the standard learning algorithm for the Boltzmann machine and may be easily extended to N-way connections. This formulation makes explicit the contribution of higher-order connections and has sparked research into analysis of the tradeoff between their increased learning power and the increased number of connections they require