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Polynomial classifiers can model decision surfaces of any shape; and yet their practical utility is limited because of the easiness with which they overfit noisy training data, and because of the sometimes impractically high number of trainable parameters. Much more popular are artificial neural networks where many simple units, called neurons, are interconnected by weighted links into larger structures of remarkably high performance.
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