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It is based on a slightly different artificial neuron (see Figure 10-4) called a linear threshold unit (LTU): the inputs and output are now numbers (instead of binary on/off values) and each input connection is associated with a weight.
The LTU computes a weighted sum of its inputs (z = w1 x1 + w2 x2 + ⋯ + wn xn = wT · x), then applies a step function to that sum and outputs the result: hw(x) = step (z) = step (wT · x).
Linear threshold unit
A single LTU can be used for simple linear binary classification.
For example, you could use a single LTU to classify iris flowers based on the petal length and width (also adding an extra bias feature x0 = 1, just like we did in previous chapters).
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