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

21 страница из 101

“Bootstrap” refers to a method of problem solving when the problem is solved by seemingly paradoxical measures (the name references Baron von Munchausen who freed the horse he was riding from a bog by pulling himself, and the horse with him, up by his bootstraps). Such algorithmic methods often involve repeated passes over the data sequence, with improved priors, or a trained filter, among other things, to have improved performance. The bootstrap amplifier from electrical engineering is an amplifier circuit where part of the output is used as input, particularly at start‐up (known as bootstrapping), allowing proper self‐initialization to a functional state (by amplifying ambient circuit noise in some cases). The bootstrap FSA proposed here is a meta‐algorithmic method in that performance “feedback” with learning is used in algorithmic refinements with iterated meta‐algorithmic learning to arrive at a functional signal acquisition status.

Acquisition is often all that is needed in a signal analysis problem, where a basic means to acquire the signals is sought, to be followed by a basic statistical analysis on those signals and their occurrences. Various methods for signal acquisition using FSA constructs are described in what follows, with focus on statistical anomalies to identify the presence of signal and “lock on” [1, 3]. The signal acquisition is initially only guided by use of statistical measures to recognize anomalies. Informatics methods and information theory measures are central to the design of a good FSA acquisition method, however, and will be reviewed in the signal acquisition context [1, 3], along with HMMs.