代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
代码结果 5,352
www.eeworm.com/read/184950/9064002
m bayesmean.m
function y = bayesmean(mu, sigma, mu0, sigma0, N, x)
% y = bayesmean(mu, sigma, mu0, sigma0, N, x)
%
% Bayesian learning of the mean of a Gaussian with known variance.
% N samples are drawn
www.eeworm.com/read/380715/9133895
m unc_n1_sin1.m
function [fval]=unc_n1_sin1(x)
%reference:
%note that you can get the formulation of unc_n1_sin1 from some
%aritcles,such as
%(1)LN de Castro, FJ Von Zuben 'Learning and optimization using the clo
www.eeworm.com/read/380715/9134055
m unc_n2_sin1.m
function [fval]=unc_n2_sin1(x)
%reference:
%note that you can get the formulation of unc_n2_sin1 from some
%aritcles,such as
%(1)LN de Castro, FJ Von Zuben 'Learning and optimization using the clo
www.eeworm.com/read/168187/9935334
m pid2.m
%Single Neural Net PID Controller based on Second Type Learning Algorithm
clear all;
close all;
xc=[0,0,0]';
K=0.02;P=2;Q=1;d=6;
xiteP=120;
xiteI=4;
xiteD=159;
%Initilizing kp,
www.eeworm.com/read/362246/10010088
m svm2.m
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le
www.eeworm.com/read/280595/10311839
m~ svm2.m~
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le
www.eeworm.com/read/280595/10311859
m svm2.m
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le
www.eeworm.com/read/159921/10587893
m mmln.m
function [mi,sigma,solution,minp,topp,N,t]=mmln(X,epsilon,tmax,t,N)
% MMLN Minimax learning for Gaussian distribution.
% [mi,sigma,solution,minp,topp,N,t]=mmln(X,epsilon,tmax,t,N)
%
% MMLN implem
www.eeworm.com/read/159628/10632320
m pid.m
%Single Neural Net PID Controller based on Second Type Learning Algorithm
clear all;
close all;
xc=[0,0,0]';
K=0.02;P=2;Q=1;d=6;
xiteP=120;
xiteI=4;
xiteD=159;
%Initilizing kp,
www.eeworm.com/read/421949/10676579
m mmln.m
function [mi,sigma,solution,minp,topp,N,t]=mmln(X,epsilon,tmax,t,N)
% MMLN Minimax learning for Gaussian distribution.
% [mi,sigma,solution,minp,topp,N,t]=mmln(X,epsilon,tmax,t,N)
%
% MMLN implem