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surf tracking
- Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by
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
freqBlind-Em
- 文章根据频率选择性衰落信道的抽头延迟线模型，将针对平坦衰落信道的CS(周期平稳过程理论)频偏盲估计 算法扩展到了频率选择性衰落信道，并通过仿真证明这种扩展是可行的.仿真结果还表明CS算法不仅有好的抗平 坦衰落能力，而且有很好的杭频率选择性衰落性能.-Article according to frequency selective fading channel tap delay line model, the flat fading channel for CS (cycle stati
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),
PPPIterative-EM-MAP-Algorithm
- iterative EM MAP algorithm
MCVEM_version1-0.tar
- This the MATLAB code that was used to produce the figures and tables in Section V of F. Forbes and G. Fort, Combining Monte Carlo and mean-field like methods for inference in Hidden Markov Random Fields, Accepted for publication in IEEE Trans. on
Algorithms_Signal_Processing
- 所上传为教程《信号处理的数学方法和算法》的配套代码。该书为读者和编程爱好者提供了广泛的应用于当代信号处理里面的数学工具。本书提供了分析，线性代数，优化，统计信号处理方面的扎实基础知识，以及配套算法代码。有趣的前沿课题包括了许多其他信号处理书籍里面的问题，比如，EM算法，盲源操作，凸集合的投影，等等，还包括许多常规的课题，比如谱估计，自适应滤波，等等。-Mathematical Methods and Algorithms for Signal Processing tackles the cha
EM_GMM-master
- em-gmm-master good for gmm algorithm