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When learning a linear function , characterized by an unknown vector such that () =, one can add the -norm of the vector to the loss expression in order to prefer solutions with smaller norms. Tikhonov regularization is one of the most common forms.
where each network module can be a linear transform, a nonlinear activation function, a convolution, etc. () is the input vector, () is the output vector from the first module, etc. BatchNorm is a module that can be inserted at any point in the feedforward network.
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [1] [2] [3] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
In probability and functional analysis, the zero norm induces a complete metric topology for the space of measurable functions and for the F-space of sequences with F–norm () / (+). [16] Here we mean by F-norm some real-valued function ‖ ‖ on an F-space with distance , such that ‖ ‖ = (,).
In mathematics, the L p spaces are function spaces defined using a natural generalization of the p-norm for finite-dimensional vector spaces.They are sometimes called Lebesgue spaces, named after Henri Lebesgue (Dunford & Schwartz 1958, III.3), although according to the Bourbaki group (Bourbaki 1987) they were first introduced by Frigyes Riesz ().
In mathematics, a Sobolev space is a vector space of functions equipped with a norm that is a combination of L p-norms of the function together with its derivatives up to a given order. The derivatives are understood in a suitable weak sense to make the space complete , i.e. a Banach space .
The function counting the number of non-zero components of a vector was called the "norm" by David Donoho. [ note 1 ] Candès et al. proved that for many problems it is probable that the L 1 {\displaystyle L^{1}} norm is equivalent to the L 0 {\displaystyle L^{0}} norm , in a technical sense: This equivalence result allows one to solve the L 1 ...
The Frobenius norm defined by ‖ ‖ = = = | | = = = {,} is self-dual, i.e., its dual norm is ‖ ‖ ′ = ‖ ‖.. The spectral norm, a special case of the induced norm when =, is defined by the maximum singular values of a matrix, that is, ‖ ‖ = (), has the nuclear norm as its dual norm, which is defined by ‖ ‖ ′ = (), for any matrix where () denote the singular values ...