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/177129/9468804
m interactive_learning.m
function D = Interactive_Learning(train_features, train_targets, params, region);
% Classify using nearest neighbors and interactive learning
% Inputs:
% features- Train features
% targets - Tr
www.eeworm.com/read/177129/9469033
m competitive_learning.m
function [features, targets, label, W] = Competitive_learning(train_features, train_targets, params, region, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% fea
www.eeworm.com/read/372113/9521128
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/372113/9521381
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/175683/9536335
m learning_svm.m
function Learning_SVM
X = [2 7; 3 6; 2 2; 8 1; 6 4; 4 8; 9 5; 9 9; 9 4; 6 9; 7 4];
Y = [ +1; +1; +1; +1; +1; -1; -1; -1; -1; -1; -1];
% define a simple artificial data set
x1ran = [
www.eeworm.com/read/362008/10023827
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/362008/10024030
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/164039/10134501
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/163511/10155671
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/357874/10199081
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