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Prior HMM‐based systems for SSA had undesirable limitations and disadvantages. For example, the speed of operation made such systems difficult, if not impossible, to use for real‐time analysis of information. In the SSA Protocol described here, distributed generalized HMM processing together with the use of the SVM‐based Classification and Clustering Methods (described next) permit the general use of the SSA Protocol free of the usual limitations. After the HMM and SSA methods are described, their synergistic union is used to convey a new approach to signal analysis with HMM methods, including a new form of stochastic‐carrier wave (SCW) communication.

Before moving on to classification and clustering (ssss1), a brief description is given of some of the theoretical foundations for learning, starting with the foundation for the choice of information measures used in ssss1–ssss1, and this is shown in ssss1. In ssss1 we then describe the theory of NNs. The ssss1 background is not meant to be a complete exposition on NN learning (the opposite), but merely goes through a few specific analyses in the area of Loss Bounds analysis to give a sense of what makes a good classification method.