Given the training set ( { x_{k},y_{k} } ), ( k = 1, 2, ldots,N ), where ( x_{k} in {mathbb{R}}^{n} ) is the kth input data in the input space and ( y_{k} in {mathbb{R}} ) represent the output variable for the given input variable (i.e., ( x_{k} )) and ( N ) refers to the number of the training samples.
However, any input that lies in the space between the two different HDE trajectories, corresponding to the two different input histories, can in principle (disregarding the effects of noise) discriminate between the two cases.
The plant is represented by a linear system in which the input matrix in the state-space representation changes depending on the input polarity.
Two-class SVMs use a maximum-margin hyperplane that separates the classes of the input vectors in the feature space.
However, in contrast to image data, input variables in the signaling space have direct biological interpretation (e.g. MAK kinase activity) and it is essential that such information is captured by low-dimensional embedding.
First, basal ganglia disinhibition potentiates how cortical modules sparsify the input in time and space.
The goal of SVR is to find the linear functional f, described by Equation 1, which maps variables of the input space in the variable output space, minimizing the Expression 2. (1) f (x ) = w, x + b with w, x ∈ ℝ d where w, x = w 1 x 1 + w 2 x 2 +... + w d x d is inner product between vectors w, x ∈ ℝ d.
Subspace clustering, also known as projected clustering and biclustering, is aimed at identifying objects that are similar in subspaces of the input space (the object space) [ 7, 8].
The optical DRPE method and its numerical simulation algorithm are first investigated in relation to the sampling considerations at various stages of the system according to the spreading of the input signal in both the space and spatial frequency domains.
Large C inputs mainly resulted from trimmed branches in the spaces between the hedges.
