Abstract:The Approximate Individual Sample Learning Entropy is based on incremental learning of a predictor
truex˜false(k+hfalse)=ϕfalse(boldxfalse(kfalse),boldwfalse), where x(k) is an input vector of a given size at time k, w is a vector of weights (adaptive parameters), and h is a prediction horizon. The basic assumption is that, after the underlying process x changes its behavior, the incrementally learning system will adapt the weights w to improve the predictor
truex˜. Our goal is to detect a change in the beha… Show more
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