搜索资源列表
HMM_CSharp
- HMM基本算法实现，包括三个基本算法。scoring,alignment,EM
matlab HMM包
- 本数据包包含了HMM经典算法，前后向重估，EM，Vertibi
PR_AI_code
- 这是《精通VC++数字图像模式识别技术及工程实践[第2版]》光盘源代码，其中包括EM算法、fisher判别函数、HMM隐马尔科夫模型、BP神经网络、小波变换、alpha-beta剪枝、A*算法等，还包含几个纹理分析、人脸定位、字符识别、车牌号识别、8数码、黑白棋、离线/在线签名等实例，因此对于学习模式识别、人工智能的朋友们都大有裨益。光盘中的素材请见另外一个资源。-This is " proficient in VC++ Digital Image Pattern Recognitio
em_ghmm
- EM算法的MATLAB实现,EM算法是HMM中用于随机过程模型参数训练的经典算法-the inplement of algorithm EM with MATLAB，the algorithm is a claasical mathod which is used to train the patameter of the HHM model.
HMM1guide
- How to use the HMM toolbox HMMs with discrete outputs Maximum likelihood parameter estimation using EM (Baum Welch)
Bayes_EM
- 利用matlab实现的基于EM算法的贝叶斯分类器的源代码，可以用来分类或识别，很值得收藏-Using matlab to achieve EM algorithm based on Bayesian classifier of the source code can be used to classification or identification, it is worthy of collection
EM-for-HMM-Multivariate-Gaussian-processes
- Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement Usage ------- [logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options])
HMM
- mm_em.m function [LL, prior, transmat, obsmat, nrIterations] = ... dhmm_em(data, prior, transmat, obsmat, varargin) LEARN_DHMM Find the ML/MAP parameters of an HMM with discrete outputs using EM. [ll_trace, prior, transmat, obsmat, iterNr]
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.
HMM
- hmm，包括前向，后向，前向-后向，EM，训练等算法的java实现-hmm, including forward, backward, forward- backward, EM, training algorithm to achieve the java
Hidden-Markov-modelling
- Hidden Markov modelling of contourlet transforms for art authentication Bayesian robust hidden Markov model Hidden Markov Models for Molecular Motors When wavelet meet HMM Hidden Markov Tree model of Contourlet Transform EM for HMM Multivar
EMfc
- EM 算法，可以解决HMM算法中的参数估计问题-EM algorithm can be solved HMM parameter estimation algorithm
EMSeg
- EM 算法是求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。-Expectation Maximization image segmentation Input: ima: gr
hmm
- hmm的程序实现包含em算法，加程序注释(HMM program implementation)
em
- em算法介绍:EM算法有很多的应用，最广泛的就是GMM混合高斯模型、聚类、HMM等等(This is the EM algorithm using JAVA, easy to understand, easy to use and helpful for understanding the EM algorithm)
chmmbox_1_2
- Matlab toolbox for Coupled Hidden Markov Modelling using Max.Likelihood EM. written by Rezek-part 1
hmmbox_3_2
- Matlab toolbox for Hidden Markov Modelling using Max.Likelihood EM. written by Rezek