Sentence examples for finding the projection from inspiring English sources

The method of least squares can be viewed as finding the projection of a vector.

Note Another iterative method termed HAAR (Haugazeau-like Averaged Alternating Reflections) for finding the projection onto intersection of finitely many closed convex sets in a Hilbert space can be found in [[20], Remark 3.4 iii)].

This is done by finding the projection length of each gene onto the subspace following eq.2, and finding the significance level using the F statistic, (7) F = l 2 k × n - k - 1 1 - l 2 where l is the projection length of the gene, n is the number of samples, and k is the dimensionality of the subspace.

The algorithm implements the parsimonious criterion of finding the linear projection of observations whose convex support has the minimum normalised perimeter.

Increasing the value of the appropriate action was a consequence of finding the right projection at the hidden layer weights (see Figure 10C).

Our constrained optimization problem for finding the best projection that incorporates both positive and negative annotations is given by (16) min ⁡ w ∑ j (1 | f j neg | ∑ z i j i ∈ f j neg − 1 | f j pos | ∑ z g j g ∈ f j pos ), s. t.   || W || 2 2 = 1, where the weights   1 / | f j neg | and 1 / | f j pos |   correct for the imbalance in the training data.

Flexibility: The algorithm should be flexible enough to run with various sparse approximation algorithm such as pursuit algorithm which involves finding the best projections of input signal onto the span of an overcomplete dictionary D. The flexibility property would enable different choices in favor of run-time constraints.

Since the projection to a high-order tensor subspace consists of several projections to the corresponding vector subspaces, the optimization can be iteratively solved by finding the k-mode projection that maximizes the scatter in the k-mode vector subspace.

And also variance filter analysis can be realized for eye detection and tracking utilising vertical projection for finding the exact location.

Furthermore, many authors have introduced the hybrid projection algorithm for finding the zero point of maximal monotones such as [12] and other references.

This work contains our dedicated study aimed to develop and complement hybrid projection algorithms for finding the common fixed points of a finite family of quasi-asymptotically pseudocontractive mappings in Hilbert spaces.