代码搜索：train
找到约 10,000 项符合「train」的源代码
代码结果 10,000
www.eeworm.com/read/289680/8535152
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/289680/8535159
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/389274/8536676
m train.m
function net = train(net, tutor, varargin)
net = train(tutor, varargin{:});
www.eeworm.com/read/389274/8537318
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
if size(y, 2) ~= 1 | ~isreal(y)
error('y must be a real double precision column vector');
end
if size(y, 1) ~= size(x, 1)
www.eeworm.com/read/188449/8539320
h train.h
#if !defined(AFX_TRAIN_H__91219D3D_0C29_11D6_A522_00000E98E3F5__INCLUDED_)
#define AFX_TRAIN_H__91219D3D_0C29_11D6_A522_00000E98E3F5__INCLUDED_
#if _MSC_VER > 1000
#pragma once
#endif // _MSC_VE
www.eeworm.com/read/188449/8539355
cpp train.cpp
// Train.cpp : implementation file
//
#include "stdafx.h"
#include "probp.h"
#include "Train.h"
#include
#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[]
www.eeworm.com/read/188280/8552112
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a support vector classifier network using the specified tutor.
%
% load data/iris x y;
%
% C = 100;
% kernel = r
www.eeworm.com/read/188280/8552154
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/188280/8552239
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/188280/8552292
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%