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[matlab] mlem DL : 1: 关于最大似然重建方法的实现，可用于tomography reconstruction-This is the code for maximum likelihood expectation maximum reconstruction method which is frequently applied in tomography reconstruction, such as CT and PET
Update : 2025-03-15 Size : 1kb Publisher : Xiubin Dai
[matlab] ExpMaxSeg DL : 0: This program is for image segmentation using Expectation maximum
Update : 2025-03-15 Size : 1kb Publisher : Dwi Maryono
[Other] emgmm DL : 0: 最大的高斯混合模型似然估计的期望最大化算法-Maximum likelihood estimation of Gaussian mixture model by expectation maximization algorithm
Update : 2025-03-15 Size : 19kb Publisher : ken
[matlab] EM DL : 0: 用于估计未知数据的EM算法，即最大期望算法，用到的地方很多，可用来做同步。-The data used to estimate the unknown EM algorithm, that is the maximum expectation algorithm, used in many places, can be used for synchronization.
Update : 2025-03-15 Size : 5kb Publisher : 龚万春
[matlab] elevator DL : 0: 实际的场景，若电梯的最大载客量m=10，设电梯中已有的客人服从0-10 之间的均匀分布，且电梯中的任意一人在任意一层下的概率相同，若你在第三层需要乘电梯到第七层，电梯处在第一层，共 8 层。且在每一层等电梯到达他们的目的楼层的客人服从0-3 的均匀分布此时我们对电梯的运行加一些限制，即电梯中若有客人未达目的地，电梯不会改变运行方向，求直到你到达第七层为止，，电梯需停次数的数学期望，并进行计算机模拟验证。-The actual scene, if the maximum lift capacity m = 10, the elevator has been designed to obey the guests uniformly distributed between 0-10, and the elevator in any one at any level under the same probability, if you The third layer needs to take the elevator to the seventh floor, the elevator in the first layer, a total of 8 layers. And in each level to reach their goals such as the elevator floor guests subject to the uniform distribution of 0-3 at this time we add some restrictions on the operation of the elevator, the elevator if it has not reached the destination guests, the lift will not change the direction, request until you reach the seventh floor so far, the elevator number of mathematical expectation to be stopped, and the computer simulation.
Update : 2025-03-15 Size : 1kb Publisher : 如是
[matlab] EMALGORITHM DL : 0: In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.-In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.
Update : 2025-03-15 Size : 2kb Publisher : loossii
[Communication-Mobile] MIMO_OFDM DL : 0: 基于期望最大化(EM)的最大后验信道估计算法(MAP)在高信噪比(SNR)下将很难获得较低的估计误差,并且,对于导频辅助的MIMO-OFDM系统,OFDM符号的数据传输效率随着发送天线的增加而明显下降.为改善这两种缺陷,引入一种等效的信号模型来改善高SNR下的估计性能在相邻多个OFDM符号内使用相移正交导频序列和联合估计来提高系统的数据传输效率和估计性能根据角域内信道间的独立性来减小噪声对估计的影响.通过仿真实验可知,所提算法具有更小的估计误差和更高的数据传输效率.-Maximum a posteriori(MAP) channel estimation algorithm usually uses expectation maximum(EM) algorithm to decrease the high computation.However,this kind of operation has a difficulty in obtaining ideal estimation performance at high signal to noise ratio(SNR) because of the convergent feature of EM algorithm.In addition,for pilot-based multiple-input multiple-output with orthogonal frequency division multiplexing(MIMO-OFDM) systems,data transmission efficiency of OFDM symbol will be reduced with the increas...
Update : 2025-03-15 Size : 131kb Publisher : 王睿
[Communication-Mobile] 123 DL : 0: 针对MIMO-OFDM系统中期望最大化(EM)信道估计算法在高信噪比(SNR)下带来的误差地板(EF)现象,且OFDM符号的数据传输效率随发射天线数的增加而明显降低,提出一种改进的高效EM信道估计算法。该算法首先引入一种准确的等效信号模型并推导出一种修正的EM算法,改善了EM算法在高SNR下的性能在多个OFDM间利用相位正交导频序列来提高数据传输效率,同时进行联合信道估计以提高估计性能。仿真实验验证了所提算法具有更高的信道估计性能和更高的数据传输效率。-For multiple-input multiple-output with orthogonal frequency division multiplexing(MIMO-OFDM) systems,the error floor(EF) phenomenon at high signal noise rate(SNR) was induced by the expectation maximum(EM) channel estimation algorithm.In addition,the data transmission efficiency was declined obviously with the increasing number of transmit antennas.According to these problems,an improved and efficient EM channel estimation algorithm was pro-posed.Firstly,an accurate and equivalent signal model was introduc...
Update : 2025-03-15 Size : 770kb Publisher : 王睿
[matlab] EM DL : 0: 求解参数估计的常用算法——EM，即期望最大化算法，用于代替样本量不完全时的极大似然估计算法。-Common algorithm for solving parameter estimation- EM, expectation maximization algorithm is used to replace the sample size is not completely at the maximum likelihood estimation algorithm.
Update : 2025-03-15 Size : 9kb Publisher : 孙磊
[Software Engineering] Fergus-Perona DL : 0: We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).
Update : 2025-03-15 Size : 3.25mb Publisher : Daria
[Algorithm] liu DL : 0: 状态模型的极大似然估计，使用EM算法，以及卡尔曼滤波。-This supplementary note discusses the maximum likelihood esti-mation of state space models using Expectation-Maximization (EM) algorithm and bootstrap procedure for statistical inference. A Matlab program script implement-ing the Kalman ¯ lter, Kalman smoother and EM algorithm
Update : 2025-03-15 Size : 2kb Publisher : 陈静雅
[AI-NN-PR] EM DL : 0: EM算法，统计中被用于寻找，依赖于不可观察的隐性变量的概率模型中，参数的最大似然估计。程序用C++实现，注释写得很清晰-Expectation-maximization algorithm，based on Maximum Likelihood Estimation,C++ program
Update : 2025-03-15 Size : 78kb Publisher : lihaoliang
[Industry research] Geometry-of-the-EM- DL : 0: 本文为对最大期望算法的一个介绍，从解析几何角度分析了算法的特性和几何意义，对从事机器学习的人有较大参考价值。-An excellent introduction for Expectation Maximum algorithm. In this paper, a geometric view of the EM algorithm is given, which might be
Update : 2025-03-15 Size : 307kb Publisher : 成方
[Other] maxwell DL : 0: Max Welling s Notes 原网页是PS格式的，我都转成了pdf格式方便阅读，机器学习算法相关，都是一些相对基础的算法，这个笔记把很多算法的精髓都整理的很清楚，适合初学者入门看，以及一些新的算法里面可能没有涉及，在英文描述中我把里面涉及的算法列了一下，按需求下载吧。-Max Welling s Notes。Statistical Estimation [ps] - bayesian estimation - maximum a posteriori (MAP) estimation - maximum likelihood (ML) estimation - Bias/Variance tradeoff & minimum description length (MDL) Expectation Maximization (EM) Algorithm [ps] - detailed derivation plus some examples Supervised Learning (Function Approximation) [ps] - mixture of experts (MoE) - cluster weighted modeling (CWM) Clustering [ps] - mixture of gaussians (MoG) - vector quantization (VQ) with k-means. Linear Models [ps] - factor analysis (FA) - probabilistic principal component analysis (PPCA) - principal component analysis (PCA) Independent Component Analysis (ICA) [ps] - noiseless ICA - noisy ICA - variational ICA Mixture of Factor Analysers (MoFA) [ps] - derivation of learning algorithm Hidden Markov Models (HMM) [ps] - viterbi decoding algorithm - Baum-Welch learning algorithm Kalman Filters (KF) [ps] - kalman filter algorithm (very detailed derivation) -
Update : 2025-03-15 Size : 1.16mb Publisher : 陈希
[Other] MLE DL : 0: This the code for maximum likelihood expectation reconstruction method which is frequently applied in tomography reconstruction-This is the code for maximum likelihood expectation reconstruction method which is frequently applied in tomography reconstruction
Update : 2025-03-15 Size : 1kb Publisher : goli
[matlab] EM算法 DL : 0: 在统计计算中，最大期望（EM）算法是在概率（probabilistic）模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐藏变量（Latent Variable）。最大期望经常用在机器学习和计算机视觉的数据聚类（Data Clustering）领域。(In statistical calculation, the expectation maximization (EM) algorithm in probability (probabilistic) maximum likelihood estimation and maximum a posteriori estimation algorithm for parameters in the model, the probability model depends on unobservable latent variable (Latent Variable). Maximum expectations are often used in machine learning and computer vision for data clustering (Data, Clustering) fields.)
Update : 2025-03-15 Size : 1kb Publisher : 橡树
[matlab] APES_EM DL : 0: 最大期望方法实现的APES，两种方法，包括一维和二维方法(Maximum expectation methods are implemented in APES, and two methods are included, one and two dimensional methods)
Update : 2025-03-15 Size : 5kb Publisher : cheerlver
[Documents] hs_err_pid5236 DL : 0: aply maximum likelihood expectation maximization
Update : 2025-03-15 Size : 5kb Publisher : alialgerian
[Algorithm] em DL : 0: 在统计计算中，最大期望（EM）算法是在概率模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐性变量。最大期望算法经常用在机器学习和计算机视觉的数据聚类（Data Clustering）领域。(In statistical computation, the maximum expectation (EM) algorithm is an algorithm to find the maximum likelihood estimation or the maximum posteriori estimation of parameters in the probability model, in which the probability model depends on the hidden variables that cannot be observed. Maximum expectation algorithm is often used in the field of machine learning and computer vision data clustering.)
Update : 2025-03-15 Size : 141kb Publisher : Ohrid