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

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The adaptive SSA ML algorithms, for real‐time analysis of the stochastic signal generated by the transducer molecule can easily offer a “lock and key” level of signal discrimination. The heart of the signal processing algorithm is a generalized Hidden Markov Model (gHMM)‐based feature extraction method, implemented on a distributed processing platform for real‐time operation. For real‐time processing, the gHMM is used for feature extraction on stochastic sequential data, while classification and clustering analysis are implemented using a SVM. In addition, the design of the ML‐based algorithms allow for scaling to large datasets, via real‐time distributed processing, and are adaptable to analysis on any stochastic sequential dataset. The ML software has also been integrated into the NTD Nanoscope [2] for “real‐time” pattern‐recognition informed (PRI) feedback [1–3] (see ssss1 for results). The methods used to implement the PRI feedback include distributed HMM and SVM implementations, which enable the processing speedup that is needed.