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

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SVMs can be used for classification and clustering (to be described in detail in ssss1), as well as aiding with signal analysis and pattern recognition on stochastic sequential data. The signal processing material described next, and in detail later, mainly draw from prior journal publications [159–189]. Analysis tools for stochastic sequential data have broad‐ranging application by making any device producing a sequence of measurements more sensitive, or “smarter,” by efficient learning of measured signal/pattern characteristics. The SVM and HMM/SVM application areas described in this book include cheminformatics, biophysics, and bioinformatics. The cheminformatics application examples pertain to channel current analysis on the alpha‐hemolysin nanopore detector (ssss1).

The biophysics and “information flows” associated with the nanopore transduction detector (NTD) in ssss1 are analyzed using a generalized set of HMM and SVM‐based tools, as well as ad hoc FSAs‐based methods, and a collection of distributed genetic algorithm methods for tuning and selection. Used with a nanopore detector, the channel current cheminformatics (CCC) for the stationary signal channel blockades (with “stationary statistics”) enables a method for a highly sensitive nanopore detector for single molecule biophysical analysis.