Tartar
发表于 2025-3-25 04:49:25
https://doi.org/10.1007/978-3-642-74816-5 high-dimensional optimization problems on directed distances (divergences), under very non-restrictive (e.g. non-convex) constraints. Such a task can be comfortably achieved by the new . method of [., .]. In the present paper, we give some new insightful details on one cornerstone of this approach.
fodlder
发表于 2025-3-25 10:16:20
http://reply.papertrans.cn/39/3837/383603/383603_22.png
Corroborate
发表于 2025-3-25 14:04:45
Héctor M. Manrique,Michael J. Walkerconfidence regions and tests for the parameter of interest, by means of minimizing empirical divergences between the considered models and the Kaplan-Meier empirical measure. This approach leads to a new natural adaptation of the empirical likelihood method to the present context of right censored d
换话题
发表于 2025-3-25 17:04:38
https://doi.org/10.1007/978-1-4615-9215-0o a nuisance parameter in the description of the model. The supremal approach based on the dual representation of CASM divergences (or .divergences) is fruitful; it leads to M-estimators with simple and standard limit distribution, and it is versatile with respect to the choice of the divergence. Du
外来
发表于 2025-3-25 23:08:43
https://doi.org/10.1007/978-94-011-6296-8-arithmetic centers. We study the invariance and equivariance properties of quasi-arithmetic centers from the viewpoint of the Fenchel-Young canonical divergences. Second, we consider statistical quasi-arithmetic mixtures and define generalizations of the Jensen-Shannon divergence according to geode
可用
发表于 2025-3-26 01:40:32
https://doi.org/10.1007/978-3-031-38271-0Information theory; Information geometry; Thermodynamics; Machine learning; Riemannian geometry; Lie grou
fledged
发表于 2025-3-26 05:16:00
http://reply.papertrans.cn/39/3837/383603/383603_27.png
隐士
发表于 2025-3-26 10:17:01
Riemannian Locally Linear Embedding with Application to Kendall Shape Spaceseen designed to learn the intrinsic structure of a set of points of a Euclidean space lying close to some submanifold. In this paper, we propose to generalise the method to manifold-valued data, that is a set of points lying close to some submanifold of a given manifold in which the points are model
Monotonous
发表于 2025-3-26 14:18:58
A Product Shape Manifold Approach for Optimizing Piecewise-Smooth Shapes algorithms in shape optimization are often based on techniques from differential geometry. Challenges arise when an application demands a non-smooth shape, which is commonly-encountered as an optimal shape for fluid-mechanical problems. In order to avoid the restriction to infinitely-smooth shapes
Suppository
发表于 2025-3-26 20:48:58
http://reply.papertrans.cn/39/3837/383603/383603_30.png