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The network does not divide the input space into discrete subspaces, but maps the inputs to displacements with a continuous function.

In addition to linear classification, SVMs can also efficiently perform non-linear classifications using a so-called kernel trick, which implicitly maps the inputs into a higher dimensional feature space.

The g k is the activation function, which maps the inputs into the real line in the closed interval [−1,1]; for example, g k (x ) = exp (2 x ) − 1 exp (2 x ) + 1 is known as the tangent hyperbolic function.

In the first step, in the hidden layer, input variables x i = (x i 1,..., x ip ) (j=1,…,p markers) are combined for each neuron (k=1,…, S neurons), using a linear function,, and subsequently transformed using a nonlinear activation function, yielding a set of inferred scores,, where g k is the activation function that maps the inputs into the real line in the closed interval [−1,1].

Similar(56)

MLP is a feed-forward ANN that maps the input data to the appropriate output.

where φ is a nonlinear function which maps the input space into a higher dimensional space.

RKS maps the input data matrix to higher dimension by multiplying it with the generated random matrix.

where φ is a nonlinear function which maps the input space into a higher-dimensional feature space.

The visible layer of the proposed model accepts BoVW-based representation of input images and then maps the input to latent topic space.

The fuzzifier maps the input data X into the fuzzy set A, Y into the fuzzy set B, and so on.

where φ is a nonlinear operator that maps the input x i into a higher-dimensional space and it is the kernel function.

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