So in the previous example, what leaves the predictor is this: Most good predictors are adaptive, so that they adjust to how "predictable" the data currently is.įor example, let's use a factor 'm' that ranges from 0 to 1024 (0 is no prediction and 1024 is full prediction).Īfter each prediction, m is adjusted up or down depending on whether the prediction was helpful or not. Then, these predicted values are compared with the actual value and the difference (error) is what gets sent to the next stage for encoding.
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