Abstract
The linear discriminant or perceptron (see Chapter 4) makes a decision
based upon a linear combination ψ(x) of the inputs,
where the c i ’s are weights, x = (x (1),..., x (d))T, and c = (c 1,..., c d )T. This is called a neural network without hidden layers (see Figure 4.1).
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© 1996 Springer Science+Business Media New York
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Devroye, L., Györfi, L., Lugosi, G. (1996). Neural Networks. In: A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0711-5_30
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DOI: https://doi.org/10.1007/978-1-4612-0711-5_30
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