Results 21 to 30 of about 4,564 (243)
IEEE Access, 2019 This paper considers a ℓ1-coefficient regularized regression algorithm with multiscale kernels based on non-independent and identically distributed (non-i.i.d.) samples.
Lu Liu, Bo Liu
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Automatic Road Extraction from Remote Sensing Images Based on Rectangle Marked Point Process
Canadian Journal of Remote Sensing, 2021 Aiming at solving inaccurate and incomplete extraction of road in remote sensing images, this paper proposes an automatic extraction algorithm based on Rectangle Marked Point Process (RMPP).
You Wu, Quanhua Zhao, Yu Li, Yiding Wang
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Polynomial ergodicity of Markov transition kernels
Stochastic Processes and their Applications, 2003 Let \(\varPhi \) be a time-homogeneous Markov chain on a countably generated measure space \((X,\mathfrak B)\) with a \(\varphi \)-irreducible and aperiodic transition kernel \(P\). Let \(f\geq 1\) be a measurable function on \(X\) and \(r=(r(n))_ {n\geq 1}\) a polynomial sequence, that is, \(\liminf r(n)(n+1)^ {-\beta }>0\), \(\limsup r(n)(n+1 ...
Fort, G., Moulines, E.
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PLoS Computational Biology, 2023 Estimating the distance at which pathogens disperse from one season to the next is crucial for designing efficient control strategies for invasive plant pathogens and a major milestone in the reduction of pesticide use in agriculture.
Hola K Adrakey +4 more
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Optimal measures and Markov transition kernels [PDF]
Journal of Global Optimization, 2012 We study optimal solutions to an abstract optimization problem for measures, which is a generalization of classical variational problems in information theory and statistical physics. In the classical problems, information and relative entropy are defined using the Kullback-Leibler divergence, and for this reason optimal measures belong to a one ...
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Accelerated training of max-margin Markov networks with kernels [PDF]
Theoretical Computer Science, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xinhua Zhang +2 more
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Meta-Feature Fusion for Few-Shot Time Series Classification
IEEE Access, 2023 Deep learning has been widely adopted for end-to-end time-series classification (TSC). However, the effectiveness of deep learning heavily relies on large-scale data.
Seo-Hyeong Park +2 more
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Applied Sciences, 2021 A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size.
Syed Taimoor Hussain Shah +10 more
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Refined n-Valued Neutrosophic Markov Decision Processes for Quality Evaluation of Talent Cultivation in Vocational Education under Emerging Productive Forces [PDF]
Neutrosophic Sets and SystemsEmerging Productive Forces (EPF) including artificial intelligence, green manufacturing, and digital platforms change skill requirements faster than classical curriculum planning can adapt.
Wei Zhao
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Duality and intertwining for discrete Markov kernels: relations and examples [PDF]
Advances in Applied Probability, 2011 We supply some relations that establish intertwining from duality and give a probabilistic interpretation. This is carried out in the context of discrete Markov chains, fixing up the background of previous relations established for monotone chains and their Siegmund duals. We revisit the duality for birth-and-death chains and the nonneutral Moran model,
Huillet, Thierry, Martinez, Servet
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