代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/415311/11077087
m nddf.m
function [D, g0, g1] = NDDF(train_features, train_targets, cost, region, test_feature)
% Classify using the normal density discriminant function
% Inputs:
% features - Train features
% target
www.eeworm.com/read/204769/15333785
cpp smoclassify.cpp
// smoClassify.cpp : Defines the entry point for the console application.
//
#include "stdafx.h"
#include "stdio.h"
#include "stdlib.h"
#include "initialize.h"
#include "classify.h"
int mai
www.eeworm.com/read/204456/15339370
m dd_label.m
function z = dd_label(x,w,realoutput)
%DD_LABEL classify the dataset and put labels in the dataset
%
% Z = DD_LABEL(X,W)
%
% Compute the output labels of objects X by mapping through mapping W
% and
www.eeworm.com/read/104144/15704294
entries
/App.inc/1.1/Mon Mar 17 07:35:48 2003//
D/BaseVCL////
D/Classify////
D/Common////
D/Components////
D/Customers////
D/DataAnalyse////
D/DepartInfo////
D/DepotBerths////
D/Employees////
D/FmMa
www.eeworm.com/read/104141/15705845
entries
/App.inc/1.1/Mon Mar 17 07:35:48 2003//
D/BaseVCL////
D/Classify////
D/Common////
D/Components////
D/Customers////
D/DataAnalyse////
D/DepartInfo////
D/DepotBerths////
D/Employees////
D/FmMa
www.eeworm.com/read/286662/8751982
m rbf_network.m
function [test_targets, mu, Wo] = RBF_Network(train_patterns, train_targets, test_patterns, Nh)
% Classify using a radial basis function network algorithm
% Inputs:
% train_patterns - Train patt
www.eeworm.com/read/286662/8751988
m rce.m
function test_targets = RCE(train_patterns, train_targets, test_patterns, lambda_m)
% Classify using the reduced coulomb energy algorithm
% Inputs:
% train_patterns - Train patterns
% train_tar
www.eeworm.com/read/383433/8947396
m svm2.m
function [test_targets, a_star] = SVM2(train_patterns, train_targets, test_patterns, kernel, ker_param, solver, slack)
% Classify using (a very simple implementation of) the support vector machine
www.eeworm.com/read/372113/9521356
m rbf_network.m
function [test_targets, mu, Wo] = RBF_Network(train_patterns, train_targets, test_patterns, Nh)
% Classify using a radial basis function network algorithm
% Inputs:
% train_patterns - Train patt
www.eeworm.com/read/372113/9521360
m rce.m
function test_targets = RCE(train_patterns, train_targets, test_patterns, lambda_m)
% Classify using the reduced coulomb energy algorithm
% Inputs:
% train_patterns - Train patterns
% train_tar