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Our goal is to maximize subject to the constraints of Proposition 1 being satisfied.
Its purpose is to maximize, subject to a budget, the size of the single largest section of connected river unimpeded by artificial flow and dispersal barriers.
Given a n-dimensional input vector, x i=(x 1,x 2,…,x n) with class labels, y i ∈ {+1,–1}, (i=1,2,..., N), the hyperplane decision function of binary SVM with kernel method is written as: and the following quadratic program: maximize subject to a i ≥ 0, and where is the number of training patters; a i are the parameters of the SVM; is a suitable kernel function, and b is the bias term.
The centroid vector of each voronoi region which can be expressed as: The new local SVM's approximation can be written as: and the following quadratic program: maximize subject to a i ≥ 0, and SVM is considered as a purely non-parametric model, whereas SV-HMM is considered as semi-parametric model as it adopts the method of grouping the associated input vectors in each class i.
Similar(56)
(i)Under Log rule, is maximized subject to.
Under this condition, MSER precoder maximizes subject to, where.
The utility function is maximized subject to the following constraints stated below.
where the data rate is maximized subject to the transmission power limit.
The objective function is minimized or maximized subject to the satisfaction of flow equations.
Then, the coding gain is maximized subject to a set of constraints on vanishing moments and frequency selectivity.
When the sum rate is maximized subject to the rate constraints (28) in Theorem 1, the equalities in (28) hold.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com