登录
首页 » matlab » hmm_matlab

hmm_matlab

于 2020-09-02 发布 文件大小:3934KB
0 249
下载积分: 1 下载次数: 88

代码说明:

  基于隐马尔科夫模型HMM的人脸识别程序,使用Matlab编写,提取的人脸识别特征为DCT系数(it is face detection codes based on HMM Model ,you can run iton Matlab,the feature from face is DCT)

文件列表:

hmm_matlab
..........\dct_hmm.m,4871,2013-04-10
..........\dct_test1.m,2320,2013-04-10
..........\em_converged.m,1189,2005-04-25
..........\fwdback.m,7194,2010-08-30
..........\isposdef.m,289,2005-04-25
..........\KPMstats
..........\........\#histCmpChi2.m#,267,2005-05-03
..........\........\beta_sample.m,1955,2005-04-25
..........\........\chisquared_histo.m,199,2005-04-25
..........\........\chisquared_prob.m,1326,2005-04-25
..........\........\chisquared_readme.txt,1389,2005-04-25
..........\........\chisquared_table.m,2127,2005-04-25
..........\........\clg_Mstep.m,5884,2005-04-25
..........\........\clg_Mstep_simple.m,1463,2005-04-25
..........\........\clg_prob.m,421,2005-04-25
..........\........\condGaussToJoint.m,646,2005-04-25
..........\........\condgaussTrainObserved.m,908,2005-04-25
..........\........\condgauss_sample.m,351,2005-04-25
..........\........\cond_indep_fisher_z.m,3789,2005-04-25
..........\........\convertBinaryLabels.m,101,2005-04-25
..........\........\cwr_demo.m,3513,2005-04-25
..........\........\cwr_em.m,4912,2005-04-25
..........\........\cwr_predict.m,1677,2005-04-25
..........\........\cwr_prob.m,1011,2005-04-25
..........\........\cwr_readme.txt,534,2005-04-25
..........\........\cwr_test.m,2436,2005-04-25
..........\........\dirichletpdf.m,1049,2005-05-22
..........\........\dirichletrnd.m,1049,2005-05-22
..........\........\dirichlet_sample.m,582,2005-04-25
..........\........\distchck.m,3836,2005-04-25
..........\........\eigdec.m,1535,2005-04-25
..........\........\est_transmat.m,535,2005-04-25
..........\........\fit_paritioned_model_testfn.m,103,2005-04-25
..........\........\fit_partitioned_model.m,2290,2005-04-25
..........\........\fwdback.m,7194,2010-08-30
..........\........\gamma_sample.m,3144,2005-04-25
..........\........\gaussian_prob.asv,852,2009-08-05
..........\........\gaussian_prob.m,853,2009-08-05
..........\........\gaussian_sample.m,659,2005-04-25
..........\........\histCmpChi2.m,394,2005-05-03
..........\........\histCmpChi2.m~,353,2005-05-03
..........\........\KLgauss.m,342,2005-04-25
..........\........\linear_regression.m,2038,2005-04-25
..........\........\logist2.m,3050,2005-04-25
..........\........\logist2Apply.m,365,2005-04-25
..........\........\logist2ApplyRegularized.m,91,2005-04-25
..........\........\logist2Fit.m,593,2005-04-25
..........\........\logist2FitRegularized.m,411,2005-04-25
..........\........\logistK.m,7540,2005-04-25
..........\........\logistK_eval.m,2372,2005-04-25
..........\........\marginalize_gaussian.m,293,2005-04-25
..........\........\matrix_normal_pdf.m,346,2005-04-25
..........\........\matrix_T_pdf.m,430,2005-04-25
..........\........\mc_stat_distrib.m,790,2005-04-25
..........\........\mhmm_em.m,5745,2010-08-30
..........\........\mhmm_logprob.m,960,2010-08-30
..........\........\mixgauss_classifier_apply.m,534,2005-04-25
..........\........\mixgauss_classifier_train.m,1377,2005-04-25
..........\........\mixgauss_em.m,2252,2005-04-25
..........\........\mixgauss_init.m,1357,2005-04-25
..........\........\mixgauss_Mstep.m,3448,2009-08-16
..........\........\mixgauss_prob.asv,4349,2009-08-16
..........\........\mixgauss_prob.m,4375,2009-08-16
..........\........\mixgauss_prob_test.m,2320,2005-04-25
..........\........\mixgauss_sample.m,734,2005-04-25
..........\........\mkPolyFvec.m,576,2005-04-25
..........\........\mk_unit_norm.m,280,2005-04-25
..........\........\multinomial_prob.m,567,2005-04-25
..........\........\multinomial_sample.m,577,2005-04-25
..........\........\multipdf.m,1192,2005-05-22
..........\........\multirnd.m,1161,2005-05-22
..........\........\normal_coef.m,205,2005-04-25
..........\........\partial_corr_coef.m,844,2005-04-25
..........\........\parzen.m,2478,2005-04-25
..........\........\parzenC.c,2790,2005-04-25
..........\........\parzenC.dll,49152,2005-04-25
..........\........\parzenC.mexglx,20215,2005-04-25
..........\........\parzenC_test.m,250,2005-04-25
..........\........\parzen_fit_select_unif.m,1668,2005-04-25
..........\........\pca.m,1077,2005-04-25
..........\........\README.txt,156,2005-04-25
..........\........\rndcheck.m,8072,2005-04-25
..........\........\sample.m,358,2005-04-25
..........\........\sample_discrete.m,1002,2005-04-25
..........\........\sample_gaussian.m,524,2005-04-25
..........\........\standardize.m,462,2005-05-03
..........\........\standardize.m~,446,2005-04-25
..........\........\student_t_logprob.m,521,2005-04-25
..........\........\student_t_prob.m,643,2005-04-25
..........\........\test_dir.m,368,2005-05-22
..........\........\unidrndKPM.m,122,2005-05-31
..........\........\unidrndKPM.m~,85,2005-05-31
..........\........\unif_discrete_sample.m,258,2005-04-25
..........\........\viterbi_path.m,1541,2010-08-30
..........\........\weightedRegression.m,1343,2005-04-25
..........\KPMtools
..........\........\approxeq.m,489,2005-04-25
..........\........\approx_unique.m,852,2005-04-25
..........\........\argmax.m,347,2005-04-25

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • filesForMATLABCentral
    Graphical evaluation for convolution
    2018-03-05 14:06:46下载
    积分:1
  • orbit.m
    Displays the orbit of a LEO satellites
    2013-01-09 00:22:10下载
    积分:1
  • SVM
     支持向量机方法是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的,根据有限的样本信息在模型的复杂性(即对特定训练样本的学习精度,Accuracy)和学习能力(即无错误地识别任意样本的能力)之间寻求最佳折衷,以期获得最好的推广能力[14](或称泛化能力)。 (SVM is based on statistical learning theory and the theory of VC dimension based on structural risk minimization principle, according to the limited sample of information in the model complexity (ie, training samples of a specific learning accuracy, Accuracy) and learning ability (ie, error-free samples to identify any capacity) to find the best compromise between, in order to obtain the best generalization ability [14] (or generalization).)
    2010-10-25 17:19:32下载
    积分:1
  • GATunedInputShaper
    The Shaper is implemented as a state machine for code production and integration in embedded systems. Shaper gets active on a rising edge of Trigger , filtering out the fast dynamic of the target in which excites all the plant modes. param.mat plant.mdl shaper.mdl shaperScan.m
    2010-12-02 12:53:56下载
    积分:1
  • feedforward_with_GUI
    design and implementation of feedforward neural network with BP training algorithm.(include the GUI)
    2009-12-29 06:36:06下载
    积分:1
  • globle_graph_mining
    说明:  图挖掘,图聚类 m文件主要包含4个功能:图的生成,图相似度分析,节点符号化,边权挖掘 ,详细说明见注释(graph mining, graph clustering)
    2009-08-31 21:33:51下载
    积分:1
  • MATLAB-newbox
    说明:  采用面向对象的基本思想,MATLAB/SIMULINK环境下,开发出一个基于MATLAB的化工单元操作工具箱(ChET)并以此工具箱为基础进行了化工过程模拟的尝试。(Adopt the basic idea of object-oriented, MATLAB/SIMULINK environment, the development of a MATLAB-based toolbox of chemical unit operation (ChET) and to the toolbox as the basis for a chemical process simulation experiment.)
    2008-11-29 13:31:56下载
    积分:1
  • linear-system-analysis
    线性系统状态空间分析(包括分析系统可控,可观性,对系统进行非奇异线性变换,分析系统稳定性,绘制阶跃响应曲线),极点配置(The state space of the linear system analysis (including analysis system controllability, observability, non-singular linear transformation of the system, the analysis of the stability of the system to draw step response curve), pole placement)
    2012-11-25 16:05:24下载
    积分:1
  • SER
    Comparison of the performances of the LS and the MMSE channel estimators
    2009-04-12 14:05:00下载
    积分:1
  • cab
    Echelon factorization A = c a b.
    2010-03-06 17:12:07下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载