Примери коришћења Vector space на Енглеском и њихови преводи на Српски
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And is a GF(p)-vector space.
In fact, the vector space of all real functions is the direct sum of the subspaces of even and odd functions.
This multiplication makes F into a GF(p)-vector space.
In some model in which there is a vector space with two bases of different cardinalities.
Sequences over a field may also be viewed as vectors in a vector space.
In linear algebra,an endomorphism of a vector space V is a linear operator V→ V.
When the vector space is finite-dimensional, the automorphism group of V is the same as the general linear group, GL(V).
Scoring, term weighting& the vector space model.
Any basis for a vector space V has the same cardinality as any other basis for that same vector space. .
In all models of ZF¬C there is a vector space with no basis.
In the context of abstract algebra, a mathematical object is an algebraic structure such as a group,ring, or vector space.
The operations of addition and multiplication in a vector space must satisfy the following axioms.
For Minkowski addition, the zero set{0}, containing only the zero vector 0, is an identity element:For every subset S, of a vector space.
Basic examples and first consequences of vector space axioms; Cartesian product of vector spaces. .
A vector space over a field F(often the field of the real numbers) is a set V equipped with two binary operations satisfying the following axioms.
On every infinite-dimensional topological vector space there is a discontinuous linear map.
For Minkowski addition, the zero set{0} containing only the zero vector 0 has special importance:For every non-empty subset S of a vector space.
There exists a model of ZF¬C in which there is a vector space with two bases of different cardinalities.
A vector space is a mathematical structure formed by a collection of elements called vectors which may be added together and multiplied scaled by numbers called scalars in this context.
A linear map from V to K(with K viewed as a vector space over itself) is called a linear functional.
Word embedding, such as word2vec, can be thought of as a representational layer in a deep-learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset;the position is represented as a point in a vector space.
So elements of this subgroup can be viewed as comprising a vector space of dimension n over the finite field of p elements Fp.
In linear algebra,an endomorphism of a vector space V is a linear operator V→ V. An automorphism is an invertible linear operator on V. When the vector space is finite-dimensional, the automorphism group of V is the same as the general linear group, GL(V).
In mathematics, Chebyshev distance(or Tchebychev distance), maximum metric, orL metric is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
For example, in the case of the Cauchy equation mentioned above, the solutions that are continuous functions are the'reasonable' ones,while other solutions that are not likely to have practical application can be constructed(by using a Hamel basis for the real numbers as vector space over the rational numbers).
The three-dimensional Euclidean space R3 is a vector space, and lines and planes passing through the origin are vector subspaces in R3.
A probability distribution whose sample space is one-dimensional(for example real numbers, list of labels, ordered labels or binary) is called univariate,while a distribution whose sample space is a vector space of dimension 2 or more is called multivariate.
It may be the case that conditions from mathematical analysis should be applied; for example, in the case of the Cauchy equation mentioned above, the solutions that are continuous functions are the'reasonable' ones,while other solutions that are not likely to have practical application can be constructed(by using a Hamel basis for the real numbers as vector space over the rational numbers).