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/455708/7368034
cpp classifydoc.cpp
// classifyDoc.cpp : implementation of the CClassifyDoc class
//
#include "stdafx.h"
#include "classify.h"
#include "classifyDoc.h"
#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
s
www.eeworm.com/read/454660/7385845
java compareints.java
// control/CompareInts.java
// TIJ4 Chapter Control, Exercise 2, page 139
/* Write a program that generates 25 random int values. For each value, use an
* if-else statement to classify it as greate
www.eeworm.com/read/434781/7801810
java compareints.java
// control/CompareInts.java
// TIJ4 Chapter Control, Exercise 2, page 139
/* Write a program that generates 25 random int values. For each value, use an
* if-else statement to classify it as greate
www.eeworm.com/read/399996/7816595
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
www.eeworm.com/read/399996/7816613
m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/399996/7816662
m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/399996/7816708
m optimal_brain_surgeon.m
function [test_targets, Wh, Wo, J] = Optimal_Brain_Surgeon(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and remov
www.eeworm.com/read/399996/7816749
m relaxation_bm.m
function [test_targets, a] = Relaxation_BM(train_patterns, train_targets, test_patterns, params)
% Classify using the batch relaxation with margin algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/399996/7816876
asv bagging.asv
function [test_targets] = Bagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/399996/7816909
m locboost.m
function [test_targets, P, theta, phi] = LocBoost(train_patterns, train_targets, test_patterns, params)
% Classify using the local boosting algorithm
% Inputs:
% train_patterns - Train patterns