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/478193/6721830
m hosmatrix.m
function A = hosmatrix(N,r)
% hosmatrix.m
%
% This function generate a matrix consists of only 1 and 0 for the
% phase estimation method proposed in the following paper.
%
% Reference:
% [1]
www.eeworm.com/read/400577/11573186
m testp.m
%TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%
www.eeworm.com/read/255755/12057976
m testp.m
%TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%
www.eeworm.com/read/150905/12249283
m testp.m
%TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%
www.eeworm.com/read/150225/12304089
m contents.m
% HMMBOX, version 4.1, I. Rezek, University of Oxford, July 2001
% Matlab toolbox for Variational estimation of Hidden Markov Models
%
% hmminit initialise HMM (for backward compatibility onl
www.eeworm.com/read/149739/12353572
m testp.m
%TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
%
www.eeworm.com/read/225922/14511081
f90 kern_reg.f90
MODULE Kernel_Regression
! General Remarks
! The subroutines glkern.f and lokern.f use an efficient and fast algorithm for
! automatically adaptive nonparametric regression estimation with a kern
www.eeworm.com/read/124397/14569802
m lpcrespitch.m
function Phat = lpcrespitch(ehat,th,minlag,maxlag)
% lpcrespitch --> Pitch estimation from prediction error sequence.
%
%
% Phat = lpcrespitch(ehat,th,minlag,maxlag)
%
%
www.eeworm.com/read/211850/15172367
m hosmatrix.m
function A = hosmatrix(N,r)
% hosmatrix.m
%
% This function generate a matrix consists of only 1 and 0 for the
% phase estimation method proposed in the following paper.
%
% Reference:
% [1]
www.eeworm.com/read/13871/284214
m contents.m
% HMMBOX, version 4.1, I. Rezek, University of Oxford, July 2001
% Matlab toolbox for Variational estimation of Hidden Markov Models
%
% hmminit initialise HMM (for backward compatibility onl