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/468922/6981928
m svm_final.m
function [ypred]=SVM_final(x,Test,y,nbclasses,fn,koptions)
% This code use to compute the SVM classifier
% This code is edited by Eng. Alaa Tharwat Abd El. Monaaim Othman from Egypt
% Teaching ass
www.eeworm.com/read/299984/7139926
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/460435/7250401
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/450608/7480071
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/441245/7672602
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/439468/7708173
m mil_train_validate.m
function run = MIL_Train_Validate(data_file, classifier)
global preprocess;
clear run;
% The statistics of dataset
% [X, Y, num_data, num_feature] = Preprocessing(D);
% num_class = length(pre
www.eeworm.com/read/439468/7708211
m mil_test_validate.m
function run = MIL_Test_Validate(data_file, classifier)
global preprocess;
clear run;
% The statistics of dataset
%[X, Y, num_data, num_feature] = Preprocessing(D);
%num_class = length(prepro
www.eeworm.com/read/397097/8069204
m dd_error.m
function e = dd_error(w,x)
%DD_ERROR compute false positive and false negative for oc_classifier
%
% e = dd_error(w,x)
%
% Compute the fraction of target objects rejected and the fraction of ou
www.eeworm.com/read/143706/12849800
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
% per ro
www.eeworm.com/read/137160/13341786
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%