代码搜索：Ho-Kashyap
找到约 29 项符合「Ho-Kashyap」的源代码
代码结果 29
www.eeworm.com/read/374442/9404739
m hk.m
%Ho-Kashyap算法，分类器训练
%该算法仍然使用平方误差准则函数J，视作w和b的函数，在迭代过程中修正w的同时，也对矢量b进行
%调整，运用最优化技术求得准则函数J关于w和b的极小值点
function [W,k]=HK(X,b,p,n)
%-----------------参数说明----------------------%
% X : 训练模式构造的增广矩阵,
www.eeworm.com/read/333940/12651660
cpp stdafx.cpp
// stdafx.cpp : 只包括标准包含文件的源文件
// Ho-Kashyap.pch 将作为预编译头
// stdafx.obj 将包含预编译类型信息
#include "stdafx.h"
// TODO: 在 STDAFX.H 中
// 引用任何所需的附加头文件，而不是在此文件中引用
www.eeworm.com/read/374442/9404737
m hk_test.m
%<mark>Ho-Kashyap</mark>算法，分类器训练
%该算法仍然使用平方误差准则函数J，视作w和b的函数，在迭代过程中修正w的同时，也对矢量b进行
%调整，运用最优化技术求得准则函数J关于w和b的极小值点
%课本p68,例3.6.1,增加两个样本，x1，x2和x3属于w1类，x4，x5和x6属于w2类，用H-K算法进行分类器训练
%--------以下是该例的样本-----------%
%x1=[ ...
www.eeworm.com/read/333940/12651665
txt readme.txt
========================================================================
控制台应用程序：Ho-Kashyap 项目概述
========================================================================
应用程序向导已为您创建了此 Ho-Kashyap
www.eeworm.com/read/191902/8417335
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/177129/9468955
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/349842/10796916
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/316604/13520501
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/359185/6352568
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/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 -