The following figure shows the decision regions of four classes. Design a classifier for these linearly inseparable classes, using a network of M-P neurons with three output units. For class i (1 ≤ i ≤ 3), classification requires that yi = 1, while yj = -1 for j ≠ i; Class 4 is recognized when yi = -1 for 1 ≤ i ≤ 3. (HINT: try a two-layer feedforward network.)
X2 Class 1 Class 2 Class 3 Class 4 4 2
•Neural Networks (NNs) are networks of neurons, for example, as found in real (i.e. biological) brains.
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•Artificial Neurons are crude approximations of the neurons found in brains. They may be physical devices, or purely mathematical constructs.
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•Artificial Neural Networks (ANNs) are networks of Artificial Neurons, and hence constitute crude approximations to parts of real brains. They may be physical devices, or simulated on conventional computers.
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•From a practical point of view, an ANN is just a parallel computational system consisting of many simple processing elements connected together in a specific way in order to perform a particular task.
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•One should never lose sight of how crude the approximations are, and how over-simplified our ANNs are compared to real brains.
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