Sentence examples for in every vector from inspiring English sources

This new method increases the hiding capacity and the computation efficiency and allows to embed into the image several bits, in every vector, in a single pass.

Each individual Χ i is a vector that contains as many parameters as the problem decision variables D. Random values are assigned to each decision parameter in every vector according to: {mathrm{X}}_{ij}^0sim Uleft({mathrm{X}}_j^{min },{mathrm{X}}_j^{max}right) (20).

Alternatively, computing optimal gene order is equivalent to identifying a route of the traveling salesman problem (TSP) in which every vector associates with a gene that has been abstracted as a virtual city [ 30- 35].

Then we consider the problem of establishing a Boolean function (an extension) f : 0, 1n → 0, 1 belonging to a given function class C, such that f is true (respectively, false) for every vector in T~ (respectively, in F~.

In step 2, a new partition is found for every vector in the removed cluster.

That is, every vector in V is of the form (lambda e_{1} + mu e_{2}).

That the span of the basis vectors is dense implies that every vector in the space can be written as the sum of an infinite series, and the orthogonality implies that this decomposition is unique.

For a matrix A to describe a linear map f: V→W, bases for both spaces must have been chosen; recall that by definition this means that every vector in the space can be written uniquely as a (finite) linear combination of basis vectors, so that written as a (column) vector v of coefficients, only finitely many entries vi are nonzero.

For every vector in the database, the range of its values, E max and E min is calculated with step δ = E max -E min /K.

Consequently, a specific structural path corresponds to the same dimension in every feature vector, enabling the learning algorithms to make sense of the feature vectors.

The classification result is weighted average of all of the terminal nodes that are reached, providing the final decision: For testing, the time complexity is O(t · n · m), where n is the number of feature vectors in the testing set, m is the number of features in every feature vector, and t is the number of decision trees considered in the model.