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

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So, given the flexibility of the SSA Protocol to “latch on” to signal that has a reasonable set of features, you might ask what is left? (Note that, all communication protocols, both natural (genomic) and man‐made, have a “reasonable” set of features.) The answer is simply when the number of features is “unreasonable” (with enumeration not even known, typically). So instead of 100 features, or maybe 1000, we now have a situation with 100 000 to 100s of millions of features (such as with sentence translation or complex image classification). Obviously Big Data is necessary to learn with such a huge number of features present, so we are truly in the realm of Big Data to even begin with such problems, but now have the Big Features issue (e.g. Big Data with Big Features, or BDwBF). What must occur in such problems is a means to wrangle the almost intractable large feature set of information to a much smaller feature set of information, e.g. an intial layer of processing is needed just to compress the feature data. In essence, we need a form of compressive feature extraction at the outset in order to not overwhelm the acquisition process. An example from the biology of the human eye, is the layer of local neural processing at the retina before the nerve impulses even travel on to the brain for further layers of neural processing.