Machine Learning Series No.6 -- EM algorithm EM算法 1.直观理解 通俗理解:https://blog.csdn.net/v_JULY_v/article/details/81708386 通俗的理解看出就是EM算法由于不知道隐变量的分布,先给出参数的随机初始值,然后根据参数,去得到隐变量的分布,然后根据隐变量和观测变量的共同分布基于最大似然去重新估计参数...
EM算法流程:1、初始化分布参数2、重复下列两个操作直到收敛: E步骤:估计隐藏变量的概率分布期望函数; M步骤:根据期望函数重新估计分布参数。 M步公式中下界函数的推导过程:EM算法一个常见的例子就是GMM模型,每个 Algorithm之EM:Expectation Maximization简介、代码实现 EM期望极大算法简介EM算法是Dempster,Laind,Rubin ...
当协方差矩阵各向同性时,w与类中心向量平行(同LDA)。 注:PCA也可通过特征值分解进行降维,把数据投影到特征值(方差)最大的方向,但降维后数据不一定可分。
Machine Learning—Mixtures of Gaussians and the EM algorithm 印象笔记同步分享:Machine Learning—Mixtures of Gaussians and the EM algorithm
Running the example fits the Gaussian mixture model on the prepared dataset using the EM algorithm. Once fit, the model is used to predict the latent variable values for the examples in the training dataset. Note: Your results may vary given the stochastic nature of the algorithm or evaluation...
PN(k)] of N individuals (particles, agents) throughout a designated number of kmax generations (Algorithm I). Full size image The differences between the various nature-inspired algorithms are pertinent to a construction of a new population P(k+1) from the current one. For example, in a ...
CryoREAD identifies phosphate, sugar and base positions in a cryo-EM map using deep learning, which are traced and modeled into a three-dimensional structure. When tested on cryo-EM maps determined at 2.0 to 5.0 Å resolution, CryoREAD built substantially more accurate models than existing ...
a data frame containing the variables in the model. maximize a character string, the score-based algorithm to be used in the “maximization” step. Seestructure learningfor details. maximize.args a list of arguments to be passed to the algorithm specified bymaximize, such asrestartfor hill-clim...
The EM Algorithm/JerryLead 1、Introduction 在西瓜书中, EM算法是贝叶斯分类的扩展内容,用以解决训练样本含有“未观测变量”即隐变量(latent variable)的情形。然而,EM算法绝不仅仅于此,其在机器学习中占有十分重要的地位,k-means算法的核心思想就是EM算法的应用。本文主要介绍EM算法的原理,并给出EM算法的简单示例。
1.1 KNN的通俗解释 何谓K近邻算法,即K-Nearest Neighbor algorithm,简称KNN算法,单从名字来猜想,可以简单粗暴的认为是:K个最近的邻居,当K=1时,算法便成了最近邻算法,即寻找最近的那个邻居。 用官方的话来说,所谓K近邻算法,即是给定一个训练数据集,对新的输入实例,在训练数据集中找到与该实例最邻近的K个实例(...