Description: 本系统特色：
1、前台完全生成静态HTML
2、栏目是无级分类的，您可以随意设置栏目，只要您能想到的，多少都可以：）
3、制作简单，由于本人以前曾经使用过动易网站管理系统，所以在功能方面向动易文章管理系统靠拢，
前台制作采用标签制，分为系统标签和用户自义义标签两种，系统标签是自代的，用户不可以修改，而自定义标签则是用户根据相应的功能自己定制出来的，固此，可以自行删除或修改！
4、秉承JAVA开源理念，本新闻发布系统开源！
5、本系统采用到的技术：JSP+Struts+JSTL+EL，我本人喜欢JSTL+EL的组合方式，他不景向页面美观！
6、mysql.sql这个文件是mysql的脚本文件，您可以用它来创建数据库！-characteristics of the system : 1. Prospects generated completely static HTML two columns is no classification. You can arbitrarily set columns, as long as you can think of, the number can be :) 3, in producing simple, As I previously hyperactive easy to use site management system, the functions easy to move closer article management system, future production system using labels, tags and systems into user-yi - yi two labels, labeling system is the generation, users can not be amended, and custom labeling is based on users corresponding function of their custom, solid of this, free to delete or modify! 4 uphold Java open source philosophy, the news release system revenue! 5, the system used to the technology : JSP Struts Script EL, I like Script EL combinations, he is not beautiful pages t Platform: |
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Author:zzhxeva |
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Description: 能够自动给大量文件按特定的大小分类.
如有500G的大量文件.可以按自定义大小和并按最佳的组合方适分发在有序独立的文件夹里
我主要用来刻录ＤＶＤ自动分文件用．-automatically to a large number of documents on the specific size classification. For 500G of the large amount of documents. Since by definition can be size and composition as the best side in an orderly distribution of the independent paper folder, I used mainly burned DVD Automatic documents . Platform: |
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Author:张小泉 |
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Description: NeHe SDK是把Nehe的教程中所介绍的所有功能，以面向对象的形式，提供给编程人员快速开发的一套编程接口。在下面的教程中，我将按NeHe SDK源码的功能分类，一步一步把这套api介绍给大家。如果你觉得有更好的学习方法，或者有其他有益的建议，请联系我。zhouwei02@mails.tsinghua.edu.cn，zhouwei506@mails.gucas.ac.cn
我在第四课的基础上，把公共的功能提取出来，并删除了特定的绘制函数，就完成了这个空白的程序框架，它可以使用基本的OpenGL绘制命令，并提供视口，文本，纹理类的接口，实用他们可以快速创建你想要的效果。
这个框架分为两个部分，启用main.cpp文件完成创建窗口，提供必需的全局变量和执行程序循环等固定的功能。在以后的演示程序中一般不在改变。draw.cpp文件完成具体的绘制操作，它随你的应用而变化。
main.cpp的所有功能在前面的课程中都详细讲解过，这里只是把整个程序结构在说明一下，让你有一个清晰的认识。下面是它的七大结构
头文件和全局变量
Windows主函数
根据用户设置配置OpenGL的窗口
创建OpenGL运行的窗口，并返回窗口的句柄
设置绘制字体的参数
程序循环
退出程序
我们一一介绍如下：-Retrieved SDK is Nehe curricula introduced in all functions to the object-oriented form, available to the programmers a rapid development of the programming interface. In the following tutorial, I will be Retrieved SDK source of functional classification, step by step to bring this api introducing to you. If you think you have a better method of study, or for some other useful suggestions, please contact me. Zhouwei02@mails.tsinghua.edu.cn. I zhouwei506@mails.gucas.ac.cn in the fourth lesson on the basis of public function extracted, and the deletion of specific mapping function, it was completed in the blank procedural framework, It can use the OpenGL rendering basic order and inspection mouth, text, and texture type of interface, Practical fast they can create the effect you want. The fr Platform: |
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Description: 并非仅仅是一本教科书——我尝试在其中装入尽可能多的信息，并按照主题进行了有序的分类。-was not just a textbook -- in which I tried to load as much information as possible. and in accordance with the theme of the orderly classification. Platform: |
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Author:程冰 |
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Description: 在symbian中使用电话监听功能时使用的头文件和库,分类的,很不容易找到我这么全的-symbian in the use of telephone bugging functional use of header files and libraries, classification, I am not so easy to find all the Platform: |
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Author:ljj |
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Description: 程序代码说明:对癌细胞颜色进行分析，和正常细胞相比，癌细胞的核颜色较深，色彩分量有较大的差异。据此，对癌细胞进行色度学分类识别。-code : right color cell analysis, and compared to normal cells, cancer cells nuclear darker in color, a color component of the larger differences. Accordingly, the cancer classification for color identification. Platform: |
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Author:mm |
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Description: 系统介绍： 1，新闻采用在线编辑器（本人改进版），从别的网页copy过来，连接和图片、falsh等可以完整保留 2，可web上传图片，小音频，视频信息或flash到服务器，直接添加到文章中 3，大部分逻辑处理都封装在java-bean，界面修改容易 4，栏目分类采用树形编码方式，无限制级别 5，完善的权限管理方式，可为每个用户自定义后台权限-system introduced : one, using online news editor (I improved version), from the other website copy up connections and pictures, stuff that can complete two reservations, web upload pictures and small audio, video or flash information to the server, directly added to article 3 Most of the logic processing package, the java-bean, interface easy modification four columns using classification tree encoding, unlimited-5, improve the competence of management, for each user-defined background competence Platform: |
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Author:wq |
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Description: 程序代码说明:对癌细胞颜色进行分析，和正常细胞相比，癌细胞的核颜色较深，色彩分量有较大的差异。据此，对癌细胞进行色度学分类识别。-code : right color cell analysis, and compared to normal cells, cancer cells nuclear darker in color, a color component of the larger differences. Accordingly, the cancer classification for color identification. Platform: |
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Author:mm |
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Description: 这是多重信号分类的经典算法，仔细阅读，希望有所帮助，欢迎各位下载！-This is a classic multiple signal classification algorithm, carefully read, I hope be helpful, to welcome you all to download! Platform: |
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Author:汉志 |
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Description: 网上收集的关于C编程的电子书，书中含有大量C源代码，是一个关于C的电子书的合集(多本，不是一本)，比较实用。
我按电子书分类在3天内上传了不下10次，都提示系统忙，而同期上传的源码却非常顺利。不得已只好按源码上传了。-Online collection of C programming e-books, the book contains a lot of C source code, is a study on the C of E-book collection (more than this, not one), more practical. I pressed the e-book classification in 3 days uploaded no less than 10 times, have prompted the system busy, over the same period of the source upload it very smoothly. Had no choice but to press source uploaded. Platform: |
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Author:YHC |
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Description: 通过idl编程实现图像的isodata分类算法，结果表明isodata在图像分类中可以取得较好的结果。-Idl programming through ISODATA image classification algorithm, ISODATA results show that in image classification can be achieved better results. Platform: |
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Author:yl |
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Description: 这是一篇有关运动目标分类的英文文献。这篇文献以离散度作为分类特征，利用时间一致性，提高了分类性能。内容也不是很深奥，相信对初学者有较大的帮助。-This is an article on the classification of moving targets in English literature. This dispersion in the literature as the classification features, use of time consistency, improve the classification performance. Content is not very profound, I believe there is more to help beginners. Platform: |
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Author:brk1985 |
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Description: 一、问题描述若要在n个城市之间建役通信网络，只福要架设n-1条级路即可．如何以最低的经济代价建设这个通信网，是一个网的最小生成树问题。二、基本要求 （1）利用克鲁斯卡尔算法求图的最小生成树。 （2）能实现教科书6.5节中定义的抽象数据类型MFSet.以此表示构造生成树过程中的连通分量。 (3 ) 以文本形式输出生成树中各条边以及他们的权值．三、需求分析 1、构造图结构。 2、利用克鲁斯卡尔算法求图的最小生成树。 3、完成生成树的输出。 -I. Description of the problem to the n cities built between the service communication networks, Fuk only n-1 to set up the way to class Article. How to minimize the economic cost of building the communications network is a network of minimum spanning tree problem. Second, the basic requirements of (1) the use of Kruskal Algorithm for Minimum Spanning Tree. (2) to achieve 6.5 in the textbook definition of abstract data types MFSet. Spanning tree structure as that in the process of component connectivity. (3) to output text in the spanning tree edges and their weights. Third, a needs analysis, structural map structure. 2, the use of Kruskal Algorithm for Minimum Spanning Tree. 3, complete the spanning tree output. Platform: |
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Description: 原创资料：大家在使用VISIO的时候一定有这样的经历吧：想找一个图，却怎么也不记得在那个分类下了。所以我干脆把VISIO 2003下的所有图都放在一个文档中了，这样找起来可就方便多了！这可是原创的哦，第一次公开发表！
下面是一部分目录：
Visio 2003形状大全
1. Web图表
1.1. 网站图形状
1.2. 网站总体设计形状
2. 地图
2.1. 道路形状
2.2. 地铁形状
2.3. 交通形状
2.4. 路标形状
2.5. 三维方向图形状
2.6. 娱乐形状
3. 电气工程
3.1. VHF-UHF-SHF
3.2. 半导体和电子管
……
17. 组织结构图
17.1. 组织结构图形状
-Original data: VISIO all the time in the use of this experience must have it: find a map, but do not remember how was in that classification. So I simply VISIO 2003 all plans are in a document, so may find it much more convenient! Oh, this is original, first published! The following is part of the directory: Visio 2003 Shapes Book 1. Web chart 1.1. Site map the shape of 1.2. Web site design shape 2. Map 2.1. Road shape 2.2. MTR 2.3 shape. Traffic shape 2.4. Signpost the shape of 2.5. Three-dimensional shape pattern 2.6 . Entertainment shape 3. Electrical Engineering 3.1. VHF-UHF-SHF 3.2. semiconductors and electron tubes ... ... 17. Organization Chart 17.1. organization chart shapes Platform: |
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Author:周善人 |
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Description: Myra is a cross-platform Ant Colony Optimization framework written in Java. It provides a specialised data mining layer to support the application of ACO to classification problems, including the implementation of Ant-Miner and cAnt-Miner algorithms. The latter is an extension of Ant-Miner, which is able to cope with continuous attributes directly - i.e. cAnt-Miner does not requires a discretization method in a preprocessing step. Platform: |
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Description: In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification is made by K-In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification is made by KNN Platform: |
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Description: 该源码实现了使用基于稀疏表示的人脸识别算法。使用GPSR作为l1模最小化方法。-This pack of code implement a imges-based face recognition using sparse representation classification. In the algorithm, i employ GPSR as tool to complete the optimization procedure of l1-minimization. Platform: |
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Author:zhang chao |
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Description: 图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。
其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？
用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。-Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram Platform: |
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Description: Leaf disease detection and classification in matlab..i t contains input images. it can classify well with a disease name. it needs to be improved in matlab Platform: |
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Author:malathimalathi |
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Search - classification in i