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基于贝叶斯网络的半监督聚类集成模型
- 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca
A-Bayesian-Approach
- In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth’s crust.We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution
Gupta-and-Chen---2010---Theory
- This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs),
Best-2---2012
- Sum-Rate Maximization in Two-Way AF MIMO Relaying: Polynomial Time Solutions to a Class of DC Programming Problems
(MAC)-LAYER-DESIGN
- In this dissertation, there are four main aspects included: energy reservation on MAC layer, secure improvement for DoS attacks on MAC layer, query processing with uncertainty for sensor systems, and throughput maximization on MAC layer for ult
paper2
- 一种基于k-核的社会网络影响最大化算法.PDF-Social networks based on the impact of nuclear k- maximization algorithm
08114237
- Multi-channel Resource Allocation towards Ergodic Rate Maximization for Underlay Device-to-Device Communications