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What you should do as part of your exploration is to learn various models of increasing complexity. Start from a simple linear model, ending in multi-layer neural networks (with non-linear activations of course). If the nonlinear models are better then that implies that your data do not follow a linear hyperplane. Also check this out for recent trends: https:...


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One kind of system you could look into are Echo State Networks (ESNs). They are relatively cheap to train and can learn to predict output signals to an arbitrary degree of precision. All you need to train the system is some labeled training data. Thus, if you have a sequence of measurements/feature values and the corresponding sequence of class labels, you ...


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