Results 191 to 200 of about 43,162 (235)

Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction. [PDF]

PLoS One
Zheng X, Liao W.
europepmc   +1 more source

Spatiotemporal evolution and analysis of influencing factors of low-carbon economy in China's Yangtze River Delta based on nighttime light remote sensing data. [PDF]

Carbon Balance Manag
Jiang J.
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Systems-Level Insights into Bronchopulmonary Dysplasia from Meta-Analysis of Genome- Scale Studies

Happs C, Khanna P, Scoynes C, Muriungi N, Alexander A, Rogers J, Lake E, Pickup M, Parkinson N, Millar J, Baillie JK, Hendry SC.   +11 more
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Operon prediction by Markov clustering

International Journal of Data Mining and Bioinformatics, 2014
The prediction of operons is a critical step for the reconstruction of biochemical and regulatory networks at the whole genome level. In this paper, a novel operon prediction model is proposed based on Markov Clustering (MCL). The model employs a graph-clustering method by MCL for prediction and does not need a classifier.
W. Du, Z. Cao, Y. Wang, Blanzieri, Enrico, C. Zhang, Y. Liang   +5 more
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Fast Markov Clustering Algorithm Based on Belief Dynamics

IEEE Transactions on Cybernetics, 2023
Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model.
Huijia Li, Wenzhe Xu, Chenyang Qiu, Jian Pei   +3 more
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Subspace distribution clustering hidden Markov model

IEEE Transactions on Speech and Audio Processing, 2001
Most contemporary laboratory recognizers require too much memory to run, and are too slow for mass applications. One major cause of the problem is the large parameter space of their acoustic models. In this paper, we propose a new acoustic modeling methodology which we call subspace distribution clustering hidden Markov modeling (SDCHMM) with the aim ...
Bocchieri, E., Mak, Brian Kan Wing
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Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM

IEEE Transactions on Neural Networks and Learning Systems, 2023
The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-
Hui Lan, Ziquan Liu, Janet H. Hsiao, Dan Yu, Antoni B. Chan   +4 more
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On Clusters in Markov Chains

2006
Motivated by the computational difficulty of analyzing very large Markov chains, we define a notion of clusters in (not necessarily reversible) Markov chains, and explore the possibility of analyzing a cluster “in vitro,” without regard to the remainder of the chain.
Nir Ailon, Steve Chien, Cynthia Dwork
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