代码搜索：ESTIMATION
找到约 3,786 项符合「ESTIMATION」的源代码
代码结果 3,786
www.eeworm.com/read/242663/12994271
asv synchro.asv
% synchronization
% estimation of the delay caused by the channel
% SNR should be estimated before using this function..
% training bits can maybe estimate this value (computations using variance
www.eeworm.com/read/137160/13341893
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e
www.eeworm.com/read/314653/13562253
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e
www.eeworm.com/read/312163/13617595
m contents.m
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li
www.eeworm.com/read/312163/13617603
m~ contents.m~
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li
www.eeworm.com/read/147682/5728068
m plot_invmodel.m
% plot_invmodel(w,h,e,D)
%
% Generates plots for evaluating the adaptive inverse
% modeling (equalization) problem.
%
% Input variables:
% w : estimated inverse model
% h : impulse r
www.eeworm.com/read/134901/5891572
m contents.m
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li
www.eeworm.com/read/134901/5891580
m~ contents.m~
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li
www.eeworm.com/read/493294/6399964
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e
www.eeworm.com/read/492929/6414249
m mlogit_d.m
% PURPOSE: An example of mlogit(),
% prt_reg().
% maximum likelihood estimation
% of multinomial logit model
%---------------------------------------------------
% USAGE: