Now, working out whether or not a neuron should fire is easy: we set the values of the input nodes to match the elements of an input vector and then use Equations (3.1) and (3.2) for each neuron. We can do this for all of the neurons, and the result is a pattern of firing and non-firing neurons, which looks like a vector of 0s and 1s, so if there are 5 neurons, as in Figure 3.2, then a typical output pattern could be (0, 1, 0, 0, 1), which means that the second and fifth neurons fired and the others did not. We compare that pattern to the target, which is our known correct answer for this input, to identif...
- Chapter 3: Neurons, Neural Networks, and Linear Discriminants
- from Machine Learning, 2nd Edition
- Publisher: Chapman and Hall/CRC
- Released: October 2014
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