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

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Thus, FSA processes allow signal regions to be identified, or “acquired,” in O(L) time. Furthermore, in that same order of time complexity, an entire panoply of statistical moments can also be computed on the signals (and used in a bootstrap learning process). The O(L) feature extraction of statistical moments on the signal region acquired may suffice for localized events and structures. For sequential information or events, however, there is often a non‐local, or extended structural, aspect to the signal sought. In these situations we need a general, powerful, way to analyze sequential signal data that is stochastic (random, but with statistics, such as average, that may be unchanging over time if “stationary,” for example). The general method for performing stochastic sequential analysis (SSA) is via HMMs, as will be extensively described in ssss1 and ssss1, and briefly summarized in ssss1 that follows. HMM approaches require an identification of “states” in the signal analysis. If an identification of states is difficult, such as in situations where there can be changes in meaning according to context, e.g. language, then HMMs may not be useful. Text and language analytics are described in ssss1 and ssss1, and briefly outlined in the next section.