Results 101 to 110 of about 14,885,883 (209)
, 2016 To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).
Alam, Md. Ashad +2 more
openaire +2 more sources
Deep Learning Predicts Hematopoietic Stem Cell Aging From 3D Chromatin Images
Aging Cell, Volume 25, Issue 10, October 2026.Deep learning identifies age‐associated chromatin architecture signatures in 3D images of hematopoietic stem cell nuclei, providing an interpretable imaging biomarker that distinguishes young from aged cells and detects chromatin rejuvenation after treatment.
Pablo Iáñez Picazo +5 more
wiley +1 more source
Differentiability in reproducing Kernel Hilbert space on the sphere
, 2012 Um espaço de Hilbert de reprodução (EHR) é um espaço de Hilbert de funções construído de maneira específica e única a partir de um núcleo positivo definido.
Jordão, Thaís, Thaís Jordão
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Reproducing kernel Hilbert space methods for modelling the discount curve
We consider the theory of bond discounts, defined as the difference between the terminal payoff of the contract and its current price. Working in the setting of finite-dimensional realizations in the HJM framework, under suitable notions of no-arbitrage, the admissible discount curves take the form of polynomial, exponential functions.
Celary, Andreas +2 more
openaire +2 more sources
International Journal for Numerical Methods in Engineering, Volume 127, Issue 18, 30 September 2026.ABSTRACT Preparing quantum states with desired amplitude distributions is a key bottleneck in the implementation of quantum linear and nonlinear dynamics solvers, including Linear Combination of Hamiltonian Simulation (LCHS) and Schrödingerization. We present a direct, closed‐form construction of Quantized Tensor Train (QTT) representations for two ...
Katsuhiro Endo, Kazuaki Z. Takahashi
wiley +1 more source
, 2020 This research work is concerned with the new numerical solutions of some essential fractional cancer tumor models, which are investigated by using reproducing kernel Hilbert space method (RKHSM).
Attia, Nourhane +3 more
core +1 more source
Motivated by the challenges related to the calibration of financial models, we consider the problem of numerically solving a singular McKean–Vlasov equation dXt=σ(t,Xt)XtvtE[vt|Xt]dWt,where Wis a Brownian motion and vis an adapted diffusion process. This
Bayer, Christian +4 more
core +1 more source
A Theorem on Reproducing Kernel Hilbert Spaces of Pairs [PDF]
, 1992 In this paper we study reproducing kernel Hilbert and Banach spaces of pairs. These are a generalization of reproducing kernel Krein spaces and, roughly speaking, consist of pairs of Hilbert (or Banach) spaces of functions in duality with respect to a ...
Alpay, Daniel, Daniel Alpay
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Reproducing kernel hilbert spaces and applications in finance
, 2016 Ο βασικός στόχος της παρούσας διπλωματικής είναι η μελέτη της λειτουργίας και των βασικών ιδιοτήτων των χώρων hilbert με πυρήνα αναπαραγωγής (RKHS) αλλά και εφαρμογές αυτού στα χρηματοοικονομικά.
Tσολάκη, Nίκη
core
A numerical approach for solving the high-order nonlinear singular Emden–Fowler type equations
Advances in Difference Equations, 2018 Reproducing kernel Hilbert space method (RKHSM) is an analytical technique, which can overcome the difficulty at the singular point of non-homogeneous, linear singular initial value problems; especially when the singularity appears on the right-hand side
Atta Dezhbord +2 more
doaj +1 more source