代码搜索：machine learning
找到约 10,000 项符合「machine learning」的源代码
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www.eeworm.com/read/389274/8537271
m learning_c.m
function d=learning_c(x,c)
%中心的学习
%x为np×ni的输入矩阵。
%c为ni×m的初始中心。
%d为ni×m训练好的中心。
d=even_k(x,c); %对输入进行聚类
tr(1)=sumsqr(d-c);
i=0;
while tr(i+1)~=0
c=d;
d=even_k(x,c);
i=i+1;
tr(i
www.eeworm.com/read/289414/8553122
gif learning-tree.gif
www.eeworm.com/read/289414/8553136
gif learning-tree.gif
www.eeworm.com/read/432455/8604573
m learning_demo.m
% Make a point move in the 2D plane
% State = (x y xdot ydot). We only observe (x y).
% Generate data from this process, and try to learn the dynamics back.
% X(t+1) = F X(t) + noise(Q)
% Y(t) = H X(
www.eeworm.com/read/432455/8604593
m learning_demo.m
% Make a point move in the 2D plane
% State = (x y xdot ydot). We only observe (x y).
% Generate data from this process, and try to learn the dynamics back.
% X(t+1) = F X(t) + noise(Q)
% Y(t) = H X(
www.eeworm.com/read/286662/8751731
m interactive_learning.m
function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params)
% Classify using nearest neighbors and interactive learning
% Inputs:
% train_patterns - Train
www.eeworm.com/read/286662/8752015
m competitive_learning.m
function [patterns, targets, label, W] = Competitive_learning(train_patterns, train_targets, params, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% patterns -
www.eeworm.com/read/285038/8874628
m learning_demo.m
% Make a point move in the 2D plane
% State = (x y xdot ydot). We only observe (x y).
% Generate data from this process, and try to learn the dynamics back.
% X(t+1) = F X(t) + noise(Q)
% Y(t) = H X(
www.eeworm.com/read/427909/8913759
m learning_demo.m
% Make a point move in the 2D plane
% State = (x y xdot ydot). We only observe (x y).
% Generate data from this process, and try to learn the dynamics back.
% X(t+1) = F X(t) + noise(Q)
% Y(t) = H X(
www.eeworm.com/read/182453/9203733
m learning_demo.m
% Make a point move in the 2D plane
% State = (x y xdot ydot). We only observe (x y).
% Generate data from this process, and try to learn the dynamics back.
% X(t+1) = F X(t) + noise(Q)
% Y(t) = H X(