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

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The FSA sequential‐data signal processing, and extraction of statistical moments on windowed data, will be shown in ssss1 to be O(L) with L the size of the data (double the data and you double the processing time). If HMMs can be used, with their introduction of states (the sequential data is described as a sequentence of “hidden” states), then the computational cost goes as O(LN²). If N = 10, then this could be 100 times more computational time to process than that of a FSA‐based O(L) computation, so the HMMs can generally be a lot more expensive in terms of computational time. Even so, if you can benefit from a HMM it is generally possible to do so, even if hardware specialization (CPU farm utilization, etc.) is required. The problem is if you do not have a strong basis for a HMM application, e.g. when there is no strong basis for delineating the states of the system of communication under study. This is the problem encounterd in the study of natural languages (where there is significant context dependency). In ssss1 we look into FSA analysis for language by doing some basic text analytics.