代码搜索：Classifier
找到约 4,824 项符合「Classifier」的源代码
代码结果 4,824
www.eeworm.com/read/397102/8067991
m minc.m
%MINC Minimum combining classifier
%
% W = minc(V)
% W = V*minc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the minimum combiner: it selects the cla
www.eeworm.com/read/397102/8068264
m meanc.m
%MEANC Averaging combining classifier
%
% W = meanc(V)
% W = V*meanc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the mean combiner: it selects the c
www.eeworm.com/read/397102/8068339
m majorc.m
%MAJORC Majority combining classifier
%
% W = majorc(V)
% W = v*majorc
%
% If V = [V1,V2,V3,...] is a stacked set of classifiers trained for
% the same classes and W is the majority combiner: it se
www.eeworm.com/read/331448/12827565
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/244790/12843801
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/143706/12850023
m test_validate.m
function run = test_validate(D, classifier)
global preprocess;
clear run;
% The statistics of dataset
[X, Y, num_data, num_feature] = Preprocessing(D);
num_class = length(preprocess.ClassSet)
www.eeworm.com/read/143706/12850363
m train_validate.m
function run = train_validate(D, classifier)
global preprocess;
clear run;
% The statistics of dataset
[X, Y, num_data, num_feature] = Preprocessing(D);
num_class = length(preprocess.ClassSet
www.eeworm.com/read/141739/12988751
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/140851/13059147
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% pe
www.eeworm.com/read/138798/13212222
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% pe