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Relevant bibliographies by topics / Kernel belief propagation

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Academic literature on the topic 'Kernel belief propagation'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Kernel belief propagation"

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Gan, Lu, Xiaoming Liu, and Ziwei Li. "Unsupervised SAR image segmentation based on kernel TMFs with belief propagation." Journal of Engineering 2019, no. 20 (2019): 6755–58. http://dx.doi.org/10.1049/joe.2019.0428.

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Li, Dongping. "AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE." Biomedical Engineering: Applications, Basis and Communications 30, no. 06 (2018): 1850038. http://dx.doi.org/10.4015/s1016237218500382.

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The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder

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Bi, Xiaobo, Jiansheng Lin, Daijie Tang, et al. "VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals." Energies 13, no. 1 (2020): 228. http://dx.doi.org/10.3390/en13010228.

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Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classificatio

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Kloumann, Isabel M., Johan Ugander, and Jon Kleinberg. "Block models and personalized PageRank." Proceedings of the National Academy of Sciences 114, no. 1 (2016): 33–38. http://dx.doi.org/10.1073/pnas.1611275114.

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Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the “seed set expansion problem”: given a subsetSof nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of “landing probabilities” of a random walk rooted at the seed set, ranking nodes accordin

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Dissertations / Theses on the topic "Kernel belief propagation"

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Razavian, Narges Sharif. "Continuous Graphical Models for Static and Dynamic Distributions: Application to Structural Biology." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/340.

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Generative models of protein structure enable researchers to predict the behavior of proteins under different conditions. Continuous graphical models are powerful and efficient tools for modeling static and dynamic distributions, which can be used for learning generative models of molecular dynamics. In this thesis, we develop new and improved continuous graphical models, to be used in modeling of protein structure. We first present von Mises graphical models, and develop consistent and efficient algorithms for sparse structure learning and parameter estimation, and inference. We compare our m

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Book chapters on the topic "Kernel belief propagation"

1

Vivet, Marc, Brais Martínez, and Xavier Binefa. "Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagation." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02172-5_20.

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