Results 111 to 120 of about 830,323 (220)

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

Image and Vision Computing, 2014
In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts ...
C. Travieso-González, Jianguo Zhang, P. Miller, J. B. Alonso   +3 more
semanticscholar   +1 more source

A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation. [PDF]

Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of ...
Maxwell L. King, Robert D. Brooks, Xibin Zhang   +2 more
core  

Research on the coupling coordination characteristics and convergence of digital finance and regional sustainable development: evidence from Chinese city clusters

Scientific Reports
The study examines the digital finance (DF) and regional sustainable development (RSD) across 90 cities within six major city clusters in China over the period from 2011 to 2020.
Qiguang An, Yongkai Wang, Qinggang Meng, Ruoyu Wang, Qian Xie   +4 more
doaj   +1 more source

Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

Remote Sensing, 2018
In this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information.
Yi Wang, Hexiang Duan
doaj   +1 more source

Anomaly Detection and Removal Using Non-Stationary Gaussian Processes

, 2015
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data.
Garnett, Roman, Osborne, Michael, Reece, Steven, Roberts, Stephen   +3 more
core  

Markov Kernels and the Conditional Extreme Value Model [PDF]

, 2012
Abstract : The classical approach to extreme value modelling for multivariate data is to assume that the joint distribution belongs to a multivariate domain of attraction. In particular, this requires that each marginal distribution be individually attracted to a univariate extreme value distribution.
David Zeber, Sidney I. Resnick
openaire   +1 more source

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

BMC Bioinformatics, 2009
Background Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as ...
Lin Lei, Wang Xiaolong, Liu Bin, Tang Buzhou, Dong Qiwen, Wang Xuan   +5 more
doaj   +1 more source

Measurement, dynamic evolution and pollution emission effects of the coupling of green finance and digital technology-evidence from China

Frontiers in Environmental Science
This study uses the game theory combination weighting method to measure the level of coordinated development of green finance and digital technology coupling in China.
Ke Liu, Ran Du, Bo Xu, Jiaxin He, Zhaoping Wang   +4 more
doaj   +1 more source

Cubature Formulas, Geometrical Designs, Reproducing Kernels, and Markov Operators

, 2005
Cubature formulas and geometrical designs are described in terms of reproducing kernels for Hilbert spaces of functions on the one hand, and Markov operators associated to orthogonal group representations on the other hand. In this way, several known results for spheres in Euclidean spaces, involving cubature formulas for polynomial functions and ...
De La Harpe, Pierre, Pache, Claude
openaire   +3 more sources

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

eLife
Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated ...
Christine Ahrends, Mark W Woolrich, Diego Vidaurre   +2 more
doaj   +1 more source