代码搜索：Patterns
找到约 8,017 项符合「Patterns」的源代码
代码结果 8,017
www.eeworm.com/read/317622/13500841
m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Voted Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/317622/13500897
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In
www.eeworm.com/read/317622/13500912
m fisherslineardiscriminant.m
function [patterns, train_targets, w] = FishersLinearDiscriminant(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the Fisher's linear discriminant
%Inputs:
% train_p
www.eeworm.com/read/317622/13500971
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/405069/11472150
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/405069/11472189
m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Voted Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/405069/11472245
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In
www.eeworm.com/read/405069/11472260
m fisherslineardiscriminant.m
function [patterns, train_targets, w] = FishersLinearDiscriminant(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the Fisher's linear discriminant
%Inputs:
% train_p
www.eeworm.com/read/405069/11472319
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/405068/11472327
m s6_3_q4.m
clear all;close all;clc
% 输入训练参数 [1 1;1 -1;-1 1;-1 -1]
train_patterns = [0.5 0.7;0.8 -0.5;-0.7 0.8;-0.9 -0.85]';
train_targets = [0 1 1 0]';
params = [2 1e-8 0.3];
% 按照‘从左到右，从下到上’的次序
% 依次将决策区域每个