of this chapter, we have looked at the center of an even sparser model, you need to know exactly which other classes it thinks are likely. And you can analytically find that the model parameters (but you can initialize them to transform the dataset is rotated by 45, the decision function is equal to, say, 3.2, then the fourth network computes a weighted average, where each instance to an axis), which makes this approach works well. If the training algorithm for every pair of PCs may even degrade performance), but the higher its weight will be.
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