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Matlab toolbox for Hidden Markov Modelling using Max. likelihood EM. Prerequisites: Matlab 5.0, System Identification Toolbox, Netlab, Marbox. LAST UPDATED: 25 Feb. 2002.
Update : 2025-02-19 Size : 700kb Publisher : 王冰

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隐马尔科夫模型对文本信息进行抽取利用C++实现-Hidden Markov Model for text information extraction using C realize
Update : 2025-02-19 Size : 15kb Publisher : longge1998

基于隐马尔可夫模型的人脸识别 CC++源代码-Based on Hidden Markov Model Face Recognition CC++ Source code
Update : 2025-02-19 Size : 3kb Publisher : wang

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hmm代码 隐马尔科夫模型 用于手势识别等-hmm code hidden Markov model for gesture recognition, etc.
Update : 2025-02-19 Size : 8.42mb Publisher : 谭谞

实现隐马尔可夫模型的viterby算法,用matlab实现的。-Hidden Markov Model to achieve viterby algorithm, achieved using matlab.
Update : 2025-02-19 Size : 34kb Publisher : 王志

speech recognition using hidden markov model making it speaker independent Using Matlab 7.5[2008b]
Update : 2025-02-19 Size : 9.57mb Publisher : kani

HMM(隐马尔可夫模型)的matlab算法代码-HMM (Hidden Markov Model) algorithm matlab code
Update : 2025-02-19 Size : 34kb Publisher : 刘子豪

# # Hidden Markov Tree Model of Contourlet Transform: MATLAB source code that implements hidden Markov tree models for wavelet and contourlet transforms. See paper Directional multiscale modeling of images using the contourlet transform.-# # Hidden Markov Tree Model of Contourlet Transform: MATLAB source code that implements hidden Markov tree models for wavelet and contourlet transforms. See paper Directional multiscale modeling of images using the contourlet transform.
Update : 2025-02-19 Size : 2.16mb Publisher : dao

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we will easily find out the agriculture region and man made region using hidden markov model
Update : 2025-02-19 Size : 119kb Publisher : saravana

a lab for hidden markov model using matlab
Update : 2025-02-19 Size : 562kb Publisher : kharmagas

Contourlet变换中的隐马尔科夫树模型程序 Hidden Markov Tree model of Contourlet Transform ,一个Matlab实现的隐马尔科夫树模型,用于计算图像的Contourlet系数。程序同时还包括图像去噪、纹理恢复的应用模型。这些程序都是基于"Directional Multiscale Modeling of Images Using the Contourlet Transform" by D. D.-Y. Po and M. N. Do,这个文章的; 同时需要Minh N. Do的Contourlet工具箱,该工具箱可以从这里http://www.ifp.uiuc.edu/~minhdo/software/得到。 -Contourlet transform the hidden Markov model procedures for Hidden Markov Tree model of Contourlet Transform, a Matlab implementation of the Hidden Markov Tree Model, used to calculate the image Contourlet factor. Program also includes denoising, texture recovery application model. These procedures are based on the " Directional Multiscale Modeling of Images Using the Contourlet Transform" by DD-Y. Po and MN Do, this article also need Minh N. Do the Contourlet toolbox, the toolbox is available here http://www.ifp.uiuc.edu/ ~ minhdo/software/receive.
Update : 2025-02-19 Size : 2.37mb Publisher : 徐英

了解隐马尔科夫模型HMM的概念、组成和需要解决的问题;掌握三个基本算法:forward算法、Viterbi算法和Baum-Welch算法,并利用matlab进行实验分析一道具体问题-Understand the concept of Hidden Markov Model HMM, composition and problems to be solved mastered three basic algorithms: forward algorithm, Viterbi algorithm and Baum-Welch algorithm and experimental analysis using matlab specific problems together
Update : 2025-02-19 Size : 151kb Publisher : laimingzhi

matlab code for channel status prediction using hidden markov model in cognitive radio
Update : 2025-02-19 Size : 1kb Publisher : suman

使用隐马尔可夫模型在小波域降噪处理运行环境Matlab-Using hidden markov model in wavelet domain noise reduction processing run Matlab environment
Update : 2025-02-19 Size : 1.09mb Publisher : Heymaw

使用隐马尔可夫模型在小波域降噪处理运行环境Matlab-Using hidden markov model in wavelet domain noise reduction processing run Matlab environment
Update : 2025-02-19 Size : 1.04mb Publisher : schegull

使用隐马尔可夫模型在小波域降噪处理运行环境Matlab-Using hidden markov model in wavelet domain noise reduction processing run Matlab environment
Update : 2025-02-19 Size : 1.04mb Publisher : functidnalihy

使用隐马尔可夫模型在小波域降噪处理运行环境Matlab(Using hidden markov model in wavelet domain noise reduction processing run Matlab environment)
Update : 2025-02-19 Size : 1.04mb Publisher : cmcke+980

The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov model (HMM). Consider there are n states in the HMM. The particular isolated speech signal is divided into finite number of frames. Every frame of the speech signal is assumed to be generated from any one of the n states. Each state is modeled as the multivariate Gaussian density function with the specified mean vector and the covariance matrix. Let the speech segment for the particular isolated word is represented as vector S. The vector S is divided into finite number of frames (say M). The i th frame is represented as Si . Every frame is generated by any of the n states with the specified probability computed using the corresponding multivariate Gaussian density model.
Update : 2025-02-19 Size : 769kb Publisher : Khan17
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