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  2. Norm (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Norm_(mathematics)

    In mathematics, a norm is a function from a real or complex vector space to the non-negative real numbers that behaves in certain ways like the distance from the origin: it commutes with scaling, obeys a form of the triangle inequality, and is zero only at the origin.

  3. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    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.

  4. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    This regularization function, while attractive for the sparsity that it guarantees, is very difficult to solve because doing so requires optimization of a function that is not even weakly convex. Lasso regression is the minimal possible relaxation of ℓ 0 {\displaystyle \ell _{0}} penalization that yields a weakly convex optimization problem.

  5. L2 - Wikipedia

    en.wikipedia.org/wiki/L2

    The L 2 space of square-integrable functions; L 2 norm; The ℓ 2 space of square-summable sequences; L 2 cohomology, a cohomology theory for smooth non-compact manifolds with Riemannian metric; L 2 (n), the family of 2-dimensional projective special linear groups on finite fields. Ridge regression, regression and regularization method also ...

  6. Normalization (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(machine...

    The FixNorm method divides the output vectors from a transformer by their L2 norms, then multiplies by a learned parameter . The ScaleNorm replaces all LayerNorms inside a transformer by division with L2 norm, then multiplying by a learned parameter g ′ {\displaystyle g'} (shared by all ScaleNorm modules of a transformer).

  7. Reproducing kernel Hilbert space - Wikipedia

    en.wikipedia.org/wiki/Reproducing_kernel_Hilbert...

    However, there are RKHSs in which the norm is an L 2-norm, such as the space of band-limited functions (see the example below). An RKHS is associated with a kernel that reproduces every function in the space in the sense that for every x {\displaystyle x} in the set on which the functions are defined, "evaluation at x {\displaystyle x} " can be ...

  8. Sobolev space - Wikipedia

    en.wikipedia.org/wiki/Sobolev_space

    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 .

  9. Hilbert–Schmidt integral operator - Wikipedia

    en.wikipedia.org/wiki/Hilbert–Schmidt_integral...

    In mathematics, a Hilbert–Schmidt integral operator is a type of integral transform.Specifically, given a domain Ω in n-dimensional Euclidean space R n, then the square-integrable function k : Ω × Ω → C belonging to L 2 (Ω×Ω) such that

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