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Exact(2)
Then, we may define an RKHS by the linear span of the set of functions.
Next, one can apply Proposition 50 to the subalgebra of su ( k ) generated by the linear span intersected with su ( k ), i.e. 〈 S 〉 ∩ su ( k ).
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As shown by Fiorentini (1995) and Gómez (1999), though, they can be made numerically identical by replacing both pre- and post-sample observations by their least squares projections onto the linear span of the sample observations.
Let (X_{B}) be the linear span of B, endowed with the topology generated by the Minkowski gauge of B, (rho _{B}).
We denote by P n the orthogonal projector of L 2 onto M n which is the linear span of { w 1, w 2, …, w n }.
The linear span and the closed linear span of any family of vectors x are denoted Span(x) and (overline {text {Span}}({boldsymbol {x}})), respectively.
Let denote the closure of the linear span of the root vectors of the linear operator.
Then A is not the linear span of its projections.
Note that, in particular, (g_i|_{A_i}) and (h_j|_{B_j}) are injective, where (A_i) is the linear span of (L_i) and (B_j) is the linear span of (R_j).
The linear span of in is The Minkowski functional of is a norm on.
Let K be the dimension of the linear span of {σ1,…,σJ}.
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by the linear relationship
by the linear programming
by the linear dependence
by the linear function
by the linear space
by the linear operator
by the linear transformation
by the linear theory
by the linear interpolation
by the linear regression
by the linear relation
by the linear fit
by the linear combination
by the linear approximation
by the linear recurrence
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