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/120147/6303455
m nnd12vl.m
function nnd12vl(cmd,arg1)
%NND12VL Variable learning rate backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==============
www.eeworm.com/read/484356/6586077
m knn.m
function [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% knn implementation
%
% USE : [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% xapp, yapp : learning data
% valY : all the Y value pos
www.eeworm.com/read/263805/11341450
m nnd12vl.m
function nnd12vl(cmd,arg1)
%NND12VL Variable learning rate backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==============
www.eeworm.com/read/406594/11439359
m nnd12vl.m
function nnd12vl(cmd,arg1)
%NND12VL Variable learning rate backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==============
www.eeworm.com/read/262186/11602481
m knn.m
function [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% knn implementation
%
% USE : [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% xapp, yapp : learning data
% valY : all the Y value pos
www.eeworm.com/read/157074/11741391
m knn.m
function [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% knn implementation
%
% USE : [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% xapp, yapp : learning data
% valY : all the Y value pos
www.eeworm.com/read/258961/11830128
html index.html
Reinforcement Learning Simulator -- User Manual
div.footer {
clear: both;
text-align: c
www.eeworm.com/read/256398/12001882
m knn.m
function [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% knn implementation
%
% USE : [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% xapp, yapp : learning data
% valY : all the Y value pos
www.eeworm.com/read/342008/12047543
m learnlm.m
function j = learnlm(p,d)
%LEARNLM Levenberg-Marquardt learning rule.
%
% LEARNLM(P,D)
% P - RxQ matrix of input (column) vectors.
% D - SxQ matrix of delta (column) vectors.
% Returns:
% Par