Machine Learning in Action:KNN Algorithm 概述 对于分类问题,最主要的任务就是找到对应数据合适的分类。而机器学习的另一项任务就是回归,比如CTR预测之类的。ml算法按照有无label可以分为有监督学习和无监督学习,对于无监督学习的算法比较经典的有聚类算法,有监督的相对来说较多,回归类算法基本都是的。按照参数有可以...
机器学习基础:期望最大化算法(Machine Learning Fundamentals: EM Algorithm) 前言EM算法和MLE算法的相同点在于,两者都需要知道确定的概率密度函数形式。 若没有隐藏变量,则可以用MLE进行估计。若数据欠缺,或存在隐含变量,则无法使用直接使用MLE进行估计,因此需要使用EM算法。 所谓的隐藏变量,指的是1. 在整个数据集中,...
Parameter Estimation for Gaussian Mixture and Hidden Markov Models http://imaging.mrc-cbu.cam.ac.uk/methods/BayesianStuff?action=AttachFile&do=get&target=bilmes-em-algorithm.pdf [2] Yida.Xu: Expectation Maximization roboticcam/machine-learning-notes [3] LongMingsheng <deep learning> lecture Mingshe...
An elegant and powerful method for finding maximum likelihood solutions for models with latent variables is called the expectation-maximization algorithm. —— From 《Pattern Recognition and Machine Learning》 § 9.2.2 例子:三硬币模型,3枚硬币分别记为A、B、C,单独抛下正面出现的概率分别是ππ,p,q,...
KMeans算法在正是GMM在极限情况(高斯分布的极限情况,狄拉克分布)下的特例,参考《Pattern Recognition and Machine Learning》中Mixture Models and EM章节 。 参考文献 李航《统计学习方法》第9章EM算法及其推广 Expectation Maximization zhiyzuo.github.io/EM/ EM Tutorial ai.stanford.edu/~chuong EM算法实现:...
Machine Learning --- GMM & QDA\LDA & EM algorithm 一、单高斯模型GSM(多元正态分布MVN) 当特征为2D时: 马氏距离=翻转坐标系下的欧式距离: 高斯分布证明(极大熵): [例]拉格朗日乘子法对q求导: 服从指数分布族: 证毕。 二、高斯混合模型GMM(多个单高斯的线性叠加,可逼近任意分布,每个高斯是一个聚类中心...
E-Step. Estimate the missing variables in the dataset. M-Step. Maximize the parameters of the model in the presence of the data. The EM algorithm can be applied quite widely, although is perhaps most well known in machine learning for use in unsupervised learning problems, such as density ...
ans=0#colID>=6代表的是连续型变量ifcolID>=6:mean=1std=1if(colID,C)incontinuousPara:curPara=continuousPara[(colID,C)]mean=curPara[0]std=curPara[1]else:#求平均值和方差 curData=X[curJudgeList,colID]mean=curData.mean()std=curData.std()#print(mean,std)#保存元素 ...
The EM Algorithm and Extensions remains the only single source to offer a complete and unified treatment of the theory, methodology, and applications of the EM algorithm. The highly applied area of statistics here outlined involves applications in regression, medical imaging, finite mixture analysis,...
EM 算法,指的是最大期望算法(Expectation Maximization Algorithm,期望最大化算法),是一种迭代算法,在统计学中被用于寻找,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。基本思想是首先随机取一个值去初始化待估计的参数值,然后不断迭代寻找更优的参数使得其似然函数比原来的似然函数大。