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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
Lecture13
- EM algorithm for adaptive multivariate Gaussian mixtures
gmm_em_graphcut
- EM algorithm for adaptive multivariate Gaussian mixtures
lec12
- EM algorithm for adaptive multivariate Gaussian mixtures
Documentation-fixed
- EM algorithm for adaptive multivariate Gaussian mixtures
em_covariances
- Using SAS/IML : This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are
EM_algorithm
- EM algorithm 讲的非常好和详细 不可多得的好资料-em algorithm which is worth reading
1
- 该文章用反卷积算法计算GSM和UMTS基站的电磁场辐射量，此方法可以消除噪声的影响，使得测量结果更精确。-In this paper, we present a new robust, fast, and accu- rate deconvolution algorithm for correcting noise disturbed mea- surements and eliminating the influence of the resolution filter of a
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),
EMfc
- 运用EM算法的几个具体的应用代码，可以进行来进行聚类，包括K-means等-EM algorithm using several specific application code, you can be clustered, including K-means, etc.
PPPIterative-EM-MAP-Algorithm
- iterative EM MAP algorithm
train
- 这是HMM算法里的训练功能的程序，是EM算法中的一种，即前向后向算法。-This is where the training function HMM algorithm procedure is an EM algorithm, the algorithm back before that.
christopher-thesis
- BAYESIAN SEGMENTATION OF THREE DIMENSIONAL IMAGES USING THE EM/MPM 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
BMSPoisInvCode_Multiscale-detection-of-localized-
- Performs EM update for iterative calculation of Bayesian multiscale MAP estimate in Nowak/Kolaczyk algorithm. Called by BMSPoisInv.m -Performs EM update for iterative calculation of Bayesian multiscale MAP estimate in Nowak/Kolaczy
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
switched-reluctance-motor-drives
- This paper presents a novel control method for the sensor-less four phase Switched Reluctance Motor drives, i.e 8 stator and 6 rotor. The sensor-less control is based on Direct Torque Control with direct flux compensa- tion (DFC) for switched
EM
- 实验报告，实现：对于混合高斯分布的情况，使用最大期望算法，通过不断计算每个样本的均值与方差，使得似然函数达到最大值。可以很好地处理满足一定概率分布的数据。 代码中通过mvnrnd()函数，设定其中的参数，产生符合混合高斯分布的一组数据集。-Lab reports, to achieve: the case of the mixed Gaussian distribution, using expectation-maximization algorithm, through continuo
EM_fisher
- EM algorithm on fisheriris
2012.李航.统计学习方法
- 《统计学习方法》是计算机及其应用领域的一门重要的学科。《统计学习方法》全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。除第1章概论和最后一章总结外，每章介绍一种方法。叙述从具体问题或实例入手，由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文