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/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
www.eeworm.com/read/357874/10199203
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/160515/10523497
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/422789/10609839
pdf learning_matlab.pdf
www.eeworm.com/read/277142/10662599
pdf brunel.learning.pdf
www.eeworm.com/read/159122/10692916
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/349842/10796711
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