Description: 1.使用Url重写成静态页，优化meta的title属性，使搜索引擎更容易识别和收录，并提高安全性。
2.采用更加快速的查询算法和和强大的多用户自定义分类，保证程序运行的高效率。
3.首页博客概要列表采用灵活的html代码和文本的选择机制，使首页显示更加多样性。
4.使用Menu控件作为菜单导航，使您可以通过Web.sitemap任意增减无级下拉菜单扩展定位您的网站页面。
5.规范n层架构和动态缓存，以避免IIS回收资源后程序的不稳定。
6.与逛一逛论坛(goago Forums) v2.0采用统一MemberShip，可以无缝整合。
7.html代码和cs，ascx文件代码全部编译成dll，所以您也不必要担心您的页面被人家安上恶意代码。
-1. Url rewritten using static pages, the optimization of meta title attribute, Search engines make it easier to identify and inclusion, and improved security. 2. The use of a more rapid algorithm and the inquiries and more powerful user-defined classification procedures to ensure the high efficiency of operation. 3. Home blog summary list of flexible html code and the text of the selection mechanism, the homepage shows more diversity. 4. Menu used as a control menu navigation, You can make arbitrary changes Web.sitemap no pull-down menu expansion positioning your website pages. 5. Norms n-tier structure and dynamic caching, in order to avoid IIS recycling resources procedures instability. 6. And to have Forum (goago Forums) v2.0 MemberShip uniform, can be seamlessly integrated. 7.html code Platform: |
Size: 784085 |
Author:无德玄 |
Hits:
Description: 线程的概念、属性；线性创建的2种方式及其联系、区别和使用场合；线程的生命周期和5种状态；线程状态控制的一般方法；线程同步的概念、原理；线程同步的场合、线程同步的条件、同步对象的选取；线程间通讯的概念，与线程同步的区别；notify和wait的使用方法。两类socket通信的概念、特点、应用场合；两类socket通信的客户和服务端的基本步骤；组播通信的概念、组播客户端服务端的基本结构。-threads concept attribute; The creation of two linear models and their connections, and the use of different occasions; Threads of the life cycle and five state; Thread the general state control; Thread synchronization concept, principle; Thread Synchronization occasions, thread synchronization, the synchronization like the selection; Thread the concept of communication, and synchronization of different threads; notify the wait and use. Two types of socket communication concept, features, applications; Two types of socket communication and customer service side of the basic steps; The concept of multicast communication, multicast client services side of the basic structure. Platform: |
Size: 7388 |
Author:rain |
Hits:
Description: 线程的概念、属性；线性创建的2种方式及其联系、区别和使用场合；线程的生命周期和5种状态；线程状态控制的一般方法；线程同步的概念、原理；线程同步的场合、线程同步的条件、同步对象的选取；线程间通讯的概念，与线程同步的区别；notify和wait的使用方法。两类socket通信的概念、特点、应用场合；两类socket通信的客户和服务端的基本步骤；组播通信的概念、组播客户端服务端的基本结构。-threads concept attribute; The creation of two linear models and their connections, and the use of different occasions; Threads of the life cycle and five state; Thread the general state control; Thread synchronization concept, principle; Thread Synchronization occasions, thread synchronization, the synchronization like the selection; Thread the concept of communication, and synchronization of different threads; notify the wait and use. Two types of socket communication concept, features, applications; Two types of socket communication and customer service side of the basic steps; The concept of multicast communication, multicast client services side of the basic structure. Platform: |
Size: 7168 |
Author:rain |
Hits:
Description: 1.使用Url重写成静态页，优化meta的title属性，使搜索引擎更容易识别和收录，并提高安全性。
2.采用更加快速的查询算法和和强大的多用户自定义分类，保证程序运行的高效率。
3.首页博客概要列表采用灵活的html代码和文本的选择机制，使首页显示更加多样性。
4.使用Menu控件作为菜单导航，使您可以通过Web.sitemap任意增减无级下拉菜单扩展定位您的网站页面。
5.规范n层架构和动态缓存，以避免IIS回收资源后程序的不稳定。
6.与逛一逛论坛(goago Forums) v2.0采用统一MemberShip，可以无缝整合。
7.html代码和cs，ascx文件代码全部编译成dll，所以您也不必要担心您的页面被人家安上恶意代码。
-1. Url rewritten using static pages, the optimization of meta title attribute, Search engines make it easier to identify and inclusion, and improved security. 2. The use of a more rapid algorithm and the inquiries and more powerful user-defined classification procedures to ensure the high efficiency of operation. 3. Home blog summary list of flexible html code and the text of the selection mechanism, the homepage shows more diversity. 4. Menu used as a control menu navigation, You can make arbitrary changes Web.sitemap no pull-down menu expansion positioning your website pages. 5. Norms n-tier structure and dynamic caching, in order to avoid IIS recycling resources procedures instability. 6. And to have Forum (goago Forums) v2.0 MemberShip uniform, can be seamlessly integrated. 7.html code Platform: |
Size: 784384 |
Author:无德玄 |
Hits:
Description: ASP.NET页面，展示Label、Literal、输入框（单行，多行，密码）、按钮、radioButton，checkBox，checkBoxList，组合框，单选和多选列表框，panel，Image，Table，ImageMAp控件，并对相应的属性和事件编程-ASP.NET page to display the Label, Literal, enter the box (single, multi-line, password), button, radioButton, checkBox, checkBoxList, combo box, radio and multi-selection list box, panel, Image, Table, ImageMAp control, and the corresponding attribute and event programming Platform: |
Size: 135168 |
Author:LT |
Hits:
Description: sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper:
Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems. Engineering Applications of Artificial Intelligence, 13(3):263-278, 2000.
rsar reads in a MIMO (Multiple Input, Multiple Output) dataset, performs RS-based feature selection on it, and returns the selected feature subset.
Four versions of the QuickReduct algorithm are supported, QuickReduct, QuickReduct III, QuickReduct IV and QuickReduct V (progressively faster implementations). QuickReduct II is a backward elimination version of QuickReduct and is not supported yet neither is exhaustive search for reducts. -sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper:
Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems. Engineering Applications of Artificial Intelligence, 13(3):263-278, 2000.
rsar reads in a MIMO (Multiple Input, Multiple Output) dataset, performs RS-based feature selection on it, and returns the selected feature subset.
Four versions of the QuickReduct algorithm are supported, QuickReduct, QuickReduct III, QuickReduct IV and QuickReduct V (progressively faster implementations). QuickReduct II is a backward elimination version of QuickReduct and is not supported yet neither is exhaustive search for reducts. Platform: |
Size: 730112 |
Author:NH |
Hits:
Description: AEWF是基于ArcEngine探索性开发的一套Web应用框架，目前实现功能：
1）地图基本操作（放大、缩小、平移、图层控制）；
2）空间选择、属性查询；
3）地图定位及高亮显示；
4）缓冲区分析；-AEWF is based on the ArcEngine exploratory development of a Web application framework, the current implementation features: 1) the basic operation of the map (zoom in, zoom, pan, layer control) 2) space selection, attribute queries 3) Map location and highlighting display 4) buffer analysis Platform: |
Size: 1186816 |
Author:bill duan |
Hits:
Description: 采用weka包实现属性选择的功能，求核求约简-Attribute selection using weka package to achieve the function of a Core demand reduction Platform: |
Size: 3072 |
Author:lmjorz1 |
Hits:
Description: In this paper we show, by means of an example of its application to the problem of house price
forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT),
a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT
drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop
from eight economic statistical series of historical measures that may impact upon house price movement Platform: |
Size: 333824 |
Author:ahmed |
Hits:
Description: eature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction Platform: |
Size: 273408 |
Author:kumar |
Hits:
Description: 选择合适的样本特征点，然后可以将特征导入svm进行分类(After the image processing, the main information is obtained by PCA transform, and then the feature of texture information selection is put forward) Platform: |
Size: 35050496 |
Author:三千世界
|
Hits:
Description: ID3算法是一种贪心算法，用来构造决策树。ID3算法起源于概念学习系统（CLS），以信息熵的下降速度为选取测试属性的标准，即在每个节点选取还尚未被用来划分的具有最高信息增益的属性作为划分标准，然后继续这个过程，直到生成的决策树能完美分类训练样例。(The ID3 algorithm is a greedy algorithm, which is used to construct a decision tree. ID3 algorithm originated from the concept of learning system (CLS), with the descending velocity of the information entropy for choosing the test attribute selection criteria, namely in each node has not yet been used to attribute with the highest information gain division as the division standard, then continues the process until the decision tree generated perfect classification of training examples.) Platform: |
Size: 2500608 |
Author:秦冰
|
Hits:
Search - attribute selection