{'fraction_non_alphanumeric': 0.022182786157941437, 'fraction_numerical': 0.0, 'mean_word_length': 5.836363636363636, '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}}
Fast Incremental Techniques for Learning Production Rule Probabilities in Radar Electronic Support

Although Stochastic context-free grammars appear promising for recognition of radar emitters, and for estimation of their respective level of threat in radar electronic support systems, well-known techniques for learning their production rule probabilities are computationally demanding. In this paper, three fast incremental alternatives, called graphical EM (gEM), tree scanning (TS), and HOLA, are compared from several perspectives - perplexity, generalization error, time and space complexity, and convergence time. Estimation of the execution time and storage requirements allows for the assessment of complexity, while computer simulation using a radar pulse data set allows to asses the other performance measures. Results indicate that gEM and TS may provide a greater level of accuracy than HOLA, and that computational complexity may be orders of magnitude lower with HOLA. Furthermore, HOLA is an on-line technique that allows for incremental learning of probabilities to reflect changes in operational environments