Abstract
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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When we view the network from above, the hidden layer is obscured by the output layer.
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More precisely, the outputs will only approach 1 and 0 because the sigmoid function is bounded by the open interval, (0, 1).
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Kubat, M. (2017). Artificial Neural Networks. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_5
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DOI: https://doi.org/10.1007/978-3-319-63913-0_5
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