代码搜索：machine learning
找到约 10,000 项符合「machine learning」的源代码
代码结果 10,000
www.eeworm.com/read/155794/11847908
pdf learning_flash.pdf
www.eeworm.com/read/344652/11869642
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/155164/11892883
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/153833/12003794
ppt modelsim_learning.ppt
www.eeworm.com/read/338859/12275926
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/131588/14136210
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/131588/14136426
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/129915/14217641
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/129915/14217794
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/230043/14308098