代码搜索：validate
找到约 3,608 项符合「validate」的源代码
代码结果 3,608
www.eeworm.com/read/235965/14038711
h ftgxval.h
/***************************************************************************/
/* */
/* ftgxval.h
www.eeworm.com/read/286738/8746182
c rtsp_validate_method.c
/* *
* $Id: RTSP_validate_method.c 75 2004-12-15 17:27:41Z mancho $
*
* This file is part of Fenice
*
* Fenice -- Open Media Server
*
* Copyright (C) 2004 by
*
* - Giampaolo Mancini
www.eeworm.com/read/167001/9986184
c rtsp_validate_method.c
/* *
* $Id: RTSP_validate_method.c 353 2006-06-06 14:40:34Z shawill $
*
* This file is part of Fenice
*
* Fenice -- Open Media Server
*
* Copyright (C) 2004 by
*
* - Giampaolo Manci
www.eeworm.com/read/359387/10152084
js jquery.validate.1.1.2.js
/*
* Form Validation: jQuery form validation plug-in v1.1.2
*
* http://bassistance.de/jquery-plugins/jquery-plugin-validation/
*
* Copyright (c) 2006 Jörn Zaefferer
*
* $Id: jquery.validate.js
www.eeworm.com/read/467652/7007290
cs validate.ascx.designer.cs
//------------------------------------------------------------------------------
//
// This code was generated by a tool.
// Runtime Version:2.0.50727.42
//
// Change
www.eeworm.com/read/467654/7007466
cs validate.ascx.designer.cs
//------------------------------------------------------------------------------
//
// This code was generated by a tool.
// Runtime Version:2.0.50727.42
//
// Change
www.eeworm.com/read/448176/7539382
r79 wm_validate.r79
www.eeworm.com/read/439468/7708173
m mil_train_validate.m
function run = MIL_Train_Validate(data_file, classifier)
global preprocess;
clear run;
% The statistics of dataset
% [X, Y, num_data, num_feature] = Preprocessing(D);
% num_class = length(pre
www.eeworm.com/read/439468/7708177
m mil_cross_validate.m
% Input pararmeter:
% data_file: data file, including the feature data and output class
function run = MIL_Cross_Validate(data_file, classifier_wrapper_handle, classifier)
global preprocess;
www.eeworm.com/read/439468/7708211
m mil_test_validate.m
function run = MIL_Test_Validate(data_file, classifier)
global preprocess;
clear run;
% The statistics of dataset
%[X, Y, num_data, num_feature] = Preprocessing(D);
%num_class = length(prepro