代码搜索：Learning

找到约 5,352 项符合「Learning」的源代码

代码结果 5,352
热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/405069/11472312

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/400251/11580009

pdf learning frompartiallysuperviseddatausingmixturemodelsandbelieffunctions.pdf

·
www.eeworm.com/read/261876/11617790

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/347363/11670944

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/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
·