Results 111 to 120 of about 2,581 (224)
The Plant Genome, Volume 19, Issue 2, June 2026.Abstract Rice (Oryza sativa) is an important staple food, feeding more than half of the global population. A feasible improvement of rice yield is necessary to meet the ever–growing food demands. Genomic selection (GS), as an advanced breeding technique, enables the prediction of phenotypes solely based on genotypic data using a constructed genomic ...
Xiankang Hu +8 more
wiley +1 more source
On the Foundational Arguments of Sufficient Dimension Reduction
WIREs Computational Statistics, Volume 18, Issue 2, June 2026.Contemporary Sufficient Dimension Reduction, a versatile method for extracting material information from data, can serve as a preprocessor for classical modeling and inference, or as a standalone theory that leads directly to statistical inference. ABSTRACT Sufficient dimension reduction (SDR) refers to supervised methods of dimension reduction that ...
R. Dennis Cook
wiley +1 more source
Information Flow in Geophysical Systems
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 6, June 2026.Abstract We present a new framework for analyzing the evolution of information in geophysical systems. Understanding how information, and its counterpart, uncertainty, propagates is central to predictability studies and has significant implications for applications such as forecast uncertainty quantification and risk management. It also offers valuable
P. J. van Leeuwen
wiley +1 more source
Factorizations of Kernels and Reproducing Kernel Hilbert Spaces
, 2016 In this talk we will explain a series of results concerning reproducing kernel Hilbert spaces, related to the factorization of their kernels. In particular, we will talk about (trivial) isometric multipliers for a large class of reproducing kernel ...
Sarkar, Jaydeb
core
Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, Volume 53, Issue 2, Page 684-711, June 2026.Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley +1 more source
Reconstruction from free-breathing cardiac MRI data using reproducing kernel Hilbert spaces
, 2010 International audienceThis paper describes a rigorous framework for reconstructing MR images of the heart, acquired continuously over the cardiac and respiratory cycle.
Odille, Freddy +4 more
core +1 more source
Safe exploration in reproducing kernel Hilbert spaces
CoRRAccepted to AISTATS ...
Abdullah Tokmak +3 more
openaire +3 more sources
Εφαρμογές των Reproducing Kernel Hilbert Spaces στη Μηχανική Μάθηση και Υλοποίηση Αλγορίθμων [PDF]
, 2014 Σύντομη αναφορά ιδιοτήτων των Reproducing Kernel Hilbert Spaces. Εφαρμογή της θεωρίας αυτής σε αλγορίθμους (Kernel LMS, Kernel RLS) με τη βοήθεια του kernel τεχνάσματος. Παρουσίαση αποτελεσμάτων από την υλοποίηση ορισμένων αλγορίθμων.Brief reference of
Παπάζογλου Άννα
core
Machine Learning Based System Identification with Binary Output Data Using Kernel Methods
Journal of Telecommunications and Information TechnologyWithin the realm of machine learning, kernel methods stand out as a prominent class of algorithms with widespread applications, including but not limited to classification, regression, and identification tasks.
Rachid Fateh +7 more
doaj +1 more source
A Kernel-Based Metric for Balance Assessment
Journal of Causal Inference, 2018 An important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in ...
Zhu Yeying +2 more
doaj +1 more source