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/313151/13595104
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/306180/13750196
cpp classifydlg.cpp
// classifyDlg.cpp : implementation file
//
#include "stdafx.h"
#include "classify.h"
#include "classifyDlg.h"
#include
using namespace std;
#ifdef _DEBUG
#define new DEBUG_NEW
www.eeworm.com/read/359185/6352568
m ho_kashyap.m
function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region)
% Classify using the using the Ho-Kashyap algorithm
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/359185/6352601
m ml_ii.m
function D = ML_II(train_features, train_targets, Ngaussians, region)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per class and returns
www.eeworm.com/read/493206/6398578
m ho_kashyap.m
function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region)
% Classify using the using the Ho-Kashyap algorithm
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/493206/6398612
m ml_ii.m
function D = ML_II(train_features, train_targets, Ngaussians, region)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per class and returns
www.eeworm.com/read/478118/6720097
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/410924/11265018
m ho_kashyap.m
function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region)
% Classify using the using the Ho-Kashyap algorithm
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/410924/11265083
m ml_ii.m
function D = ML_II(train_features, train_targets, Ngaussians, region)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per class and returns
www.eeworm.com/read/405069/11472161
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