I'm trying to understand how a single layer perceptron would separate the feature space into classes. Let's assume the single layer perceptron consists of 3 neurons which take a two dimensional feature vector, computes the activation state, applies the activation function on it and returns a class label. The question is, whether I can predict HOW the feature space will be separated into classes.
How do a single layer perceptron separate the feature space
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