Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн

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It is common to need to acquire a signal where the signal properties are not known, or the signal is only suspected and not discovered yet, or the signal properties are known but they may be too much trouble to fully enumerate. There is no common solution, however, to the acquisition task. For this reason the initial phases of acquisition methods unavoidably tend to be ad hoc. As with data dependency in non‐evolutionary search metaheuristics (where there is no optimal search method that is guaranteed to always work well), here there is no optimal signal acquisition method known in advance. In what follows methods are described for bootstrap optimization in signal acquisition to enable the most general‐use, almost “common,” solution possible. The bootstrap algorithmic method involves repeated passes over the data sequence, with improved priors, and trained filters, among other things, to have improved signal acquisition on subsequent passes. The signal acquisition is guided by statistical measures to recognize anomalies. Informatics methods and information theory measures are central to the design of a good finite state automata (FSAs) acquisition method, and will be reviewed in signal acquisition context in ssss1–ssss1. Code examples are given in Python and C (with introductory Python described in ssss1 and ssss1). Bootstrap acquisition methods may not automatically provide a common solution, but appear to offer a process whereby a solution can be improved to some desirable level of general‐data applicability.