fit the data better. Manifold regularization adds a second regularization term, the intrinsic regularizer, to the ambient regularizer used in standard Tikhonov...

along a low-dimensional submanifold, such as manifold sculpting, manifold alignment, and manifold regularization. The major implications of this hypothesis...

process models, information regularization, and entropy minimization (of which TSVM is a special case). Laplacian regularization has been historically approached...

In mathematics and theoretical physics, zeta function regularization is a type of regularization or summability method that assigns finite values to divergent...

manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially existing across non-linear manifolds which...

project) Manifold regularization Margin-infused relaxed algorithm Margin classifier Mark V. Shaney Massive Online Analysis Matrix regularization Matthews...

extension is particularly important in multi-task learning and manifold regularization. The main difference is that the reproducing kernel Γ {\displaystyle...

M as a Riemannian manifold can be recovered from this data. This suggests that one might define a noncommutative Riemannian manifold as a spectral triple...

can be achieved by this regularization but it also introduces blurring effect, which is the main drawback of regularization. A prior knowledge of noise...

generalized to all possible regularization schemes, not just lattice regularization. This general no-go theorem states that no regularized chiral fermion theory...

invariance, a Pauli–Villars regularization of such diagrams is possible while preserving the symmetry. Whenever the regularization of a diagram is consistent...

In mathematics, it has been used to calculate knot invariants and three-manifold invariants such as the Jones polynomial. Particularly, Chern–Simons theory...

of the combinatorial boundary of the polyhedron is 2. The combinatorial manifold model of solidity also guarantees the boundary of a solid separates space...

general way) replace the domain C of multi-valued functions by a suitable manifold in C × C called Riemann surface. While this removes multi-valuedness, one...

k-sparse autoencoder. Instead of forcing sparsity, we add a sparsity regularization loss, then optimize for min θ , ϕ L ( θ , ϕ ) + λ L sparse ( θ , ϕ )...

function regularization. It was introduced by Atiyah, Patodi, and Singer (1973, 1975) who used it to extend the Hirzebruch signature theorem to manifolds with...

required the stronger assumption that the underlying closed Riemannian manifold has nonnegative sectional curvature and parallel Ricci tensor (such as...

general physical laws acts in the training of neural networks (NNs) as a regularization agent that limits the space of admissible solutions, increasing the...

origin) often used to study the extrinsic differential geometry of smooth manifolds embedded in a homogeneous space. In lay terms, a frame of reference is...

finite fields. Ridge regression, regression and regularization method also known as L2 regularization a variety of low-alloy special purpose steel a L-carrier...

notions for holomorphic line bundles on projective varieties. Demailly's regularization theorem says, in particular, that any big class can be represented by...

discreteness is thought to be fundamental, while in CDT it is viewed as a regularization of the path integral, to be removed by the continuum limit. Another...

the handful of ministers cannot be expected to deal personally with the manifold problems of modern administration. Thus, ministers set the policy, and...

S2CID 1043277. Smola, Alexander J.; Kondor, Risi (2003). "Kernels and regularization on graphs". In Schölkopf, Bernhard; Warmuth, Manfred K. (eds.). Learning...

in Riemannian geometry concerning the structure of complete Riemannian manifolds of non-positive sectional curvature Cartan–Hadamard conjecture, an open...

spacetime (Minkowski space) to curved spacetime, a general Lorentzian manifold. In full generality the equation can be defined on M {\displaystyle M}...

perform some kind of regularization. The most popular of which for computing functional determinants is the zeta function regularization. For instance, this...

for manifolds and the universal approximation theorem for neural networks. To regularize the flow f {\displaystyle f} , one can impose regularization losses...

torsion) is a topological invariant of manifolds introduced by Kurt Reidemeister (Reidemeister 1935) for 3-manifolds and generalized to higher dimensions...

l_{1}} ⁠-regularization techniques, such as sparse regression, LASSO, and ⁠ l 1 {\displaystyle l_{1}} ⁠-SVM Regularized trees, e.g. regularized random forest...