搜索资源列表
ghmm-0.7.0a.tar
- 隐马尔科夫模型及其在语音处理中的应用，这个是GNU提供的算法库，类unix环境，C语言，当前最新版本已经不再提供C＋＋的了。-Hidden Markov Model and the voice processing applications, this is the GNU algorithm library, unix environment category, C language, the current latest version is no longer available in C
modhmmts
- 马尔科夫模型的java版本实现，用于生物序列分析的好的参考-Markov model of java versions for Biological Sequence Analysis of good reference
mrbayes-3.1.2.tar
- 利用贝叶斯算法，结合马尔科夫链对系统发育的分析-Bayesian algorithm, combined with Markov chain analysis of the phylogenetic
systemmodeldesign
- 关于马尔科夫模型的构造和建模非常的使用，而且也给出了论文作为辅助-Markov model on the structure and the use of modeling is, but also gives the paper as an aid
HMMVB6
- Hidden Markov Models, the Viterbi Algorithm, and CpG Islands (in VB6)
Bioinformatics
- 生物信息学课件 基于后缀数的算法 包括了String algortm，Hidden Markov model以及App of DNA sequence-Bioinformatics suffix number of algorithms based courseware including String algortm, Hidden Markov model and the App of DNA sequence
HMM1
- HMM是隐马氏模型，预测蛋白质的二级结构，当你输入一段未知的需要测定的蛋白质序列时，利用已经训练好的蛋白质，可以预测蛋白质的二级结构 -HMM is a hidden Markov model, the predicted secondary structure of the protein, when you enter a need to determine the unknown protein sequence, the use of has trained proteins, th
Fifield-RemoteOperatingSystemDetection
- A non-parametric method for texture synthesis proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel giv
hmm-neural
- 使用hmm隐马尔科夫模型 进行语音识别,论文效果不错-Hmm using hidden Markov model for speech recognition
viterbiaEM
- 1.用隐马尔科夫模型（HMM）模拟肿瘤细胞整个染色体的拷贝数（CN）变异。并用viteri算法得到最可能的（CN）状态转移序列； 2.使用baum welch算法根据所给序列数据和初始状态转移矩阵，重新估算状态转移矩阵。-HMM, Hidden Markov, baulm welch, viterbi, SNP-array
face-recognition-SVD-HMM
- an efficient system for face recognition based on hidden markov models and SVD as features extractor
back_hmm
- An implementation of forward–backward algorithm. This algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions.