# affine combination in a sentence

- One says also that g is an "'
*affine combination*"'of the a _ i with coefficients \ lambda _ i. - The difference space can be identified with the set of " formal differences ", modulo the relation that formal differences respect
*affine combinations*in an obvious way. - The affine span of is the set of all ( finite )
*affine combinations*of points of, and its direction is the linear span of the for and in. - In other words, the only " well-behaved " centers which satisfy Archimedes'Lemma are the
*affine combinations*of the circumcenter of mass and center of mass. - *PM : approximate non-linear transformation of
*affine combination*, id = 9088 new !-- WP guess : approximate non-linear transformation of affine combination-- Status: - It's difficult to find
*affine combination*in a sentence. - *PM : approximate non-linear transformation of affine combination, id = 9088 new !-- WP guess : approximate non-linear transformation of
*affine combination*-- Status: - Similarly, one can consider
*affine combinations*, conical combinations, and convex combinations to correspond to the sub-operads where the terms sum to 1, the terms are all non-negative, or both, respectively. - Thus the predicted class is an
*affine combination*of the classes of every other point, weighted by the softmax function for each j \ in C _ j where C _ j is now the entire transformed data set. - While Alice knows the " linear structure ", both Alice and Bob know the " affine structure " i . e . the values of
*affine combinations*, defined as linear combinations in which the sum of the coefficients is 1. - When a stochastic matrix,, acts on a column vector, " " ", the result is a column vector whose entries are
*affine combinations*of " " " with coefficients from the rows in. - Linear and
*affine combinations*can be defined over any field ( or ring ), but conical and convex combination require a notion of " positive ", and hence can only be defined over an ordered field ( or ordered ring ), generally the real numbers. - The dual vector space of is naturally isomorphic to an ( " n " + 1 )-dimensional vector space which is the free vector space on "'A "'modulo the relation that
*affine combination*in "'A "'agrees with affine combination in. - The dual vector space of is naturally isomorphic to an ( " n " + 1 )-dimensional vector space which is the free vector space on "'A "'modulo the relation that affine combination in "'A "'agrees with
*affine combination*in. - If, on the other hand, the kernel assumes negative values, such as the sinc function, then the value of the filtered signal will instead be an
*affine combination*of the input values, and may fall outside of the minimum and maximum of the input signal, resulting in undershoot and overshoot. - These concepts often arise when one can take certain linear combinations of objects, but not any : for example, probability distributions are closed under convex combination ( they form a convex set ), but not conical or
*affine combinations*( or linear ), and positive measures are closed under conical combination but not affine or linear hence one defines signed measures as the linear closure.