任命Modest为 COM/DCOM/COM+ 子版版主

TechnoFantasy 2006-04-28 09:36:20

希望大家支持他的工作，也希望Modest为VB版做出更多贡献。

BTW，Modest的申请帖：
http://community.csdn.net/Expert/topic/4719/4719789.xml?temp=.1513941

...全文

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pcwe2002 2006-08-15

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恭喜

cszhh 2006-07-22

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矢敬,原来碰上版主了!

fh88615001 2006-07-05

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恭喜～

射天狼 2006-06-23

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恭喜～

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║　免费的源码、工具网站，欢迎大家访问！║
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jobs002 2006-06-13

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支持

TechnoFantasy 2006-06-01

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结贴，儿童节快乐！

leongwong 2006-05-31

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双手赞同！群众的眼睛是雪亮的！

LPH06 2006-05-31

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支持......

Arqui 2006-05-23

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恭喜

mxfeng 2006-05-22

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星光灿烂啊!!!!
星光灿烂啊!!!!
星光灿烂啊!!!!
星光灿烂啊!!!!
星光灿烂啊!!!!
星光灿烂啊!!!!
星光灿烂啊!!!!
星光灿烂啊!!!!

jackcaixia 2006-05-22

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恭喜~~接分～～

迈克揉索芙特 2006-05-22

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//支持modest，她很卖力！

她??

YrLijon 2006-05-20

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killl 2006-05-20

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支持modest，她很卖力！

zgw 2006-05-19

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恭喜,接分

a97191 2006-05-19

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ilove8 2006-05-18

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恭喜,接

ad_lee 2006-05-18

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恭喜,接分

myredit 2006-05-18

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恭喜,接分

lxcy 2006-05-18

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300分哦，，先恭喜，后接分

本参考说明了在 Microsoft c + + 编译器中实现的 c + + 编程语言。自创建以来，C++ 即已成为世界上最常用的编程语言之一。正确编写的 C++ 程序快速、高效。相对于其他语言，该语言更加灵活：它可以在最高的抽象级别上运行，还可以在硅级低级别上运行。 C++ 提供高度优化的标准库。它支持访问低级别硬件功能，从而最大限度地提高速度并最大程度地降低内存需求。可以使用 C++ 创建各种应用。游戏、设备驱动程序和高性能科学软件。嵌入式程序。 Windows 客户端应用。甚至用于其他编程语言的库和编译器也使用 C++ 编写。

Bayesian statistics has been around for more than 250 years now. During this time it has enjoyed as much recognition and appreciation as disdain and contempt. Through the last few decades it has gained more and more attention from people in statistics and almost all other sciences, engineering, and even outside the walls of the academic world. This revival has been possible due to theoretical and computational developments. Modern Bayesian statistics is mostly computational statistics. The necessity for exible and transparent models and a more interpretation of statistical analysis has only contributed to the trend. Here, we will adopt a pragmatic approach to Bayesian statistics and we will not care too much about other statistical paradigms and their relationship to Bayesian statistics. The aim of this book is to learn about Bayesian data analysis with the help of Python. Philosophical discussions are interesting but they have already been undertaken elsewhere in a richer way than we can discuss in these pages. We will take a modeling approach to statistics, we will learn to think in terms of probabilistic models, and apply Bayes' theorem to derive the logical consequences of our models and data. The approach will also be computational; models will be coded using PyMC3—a great library for Bayesian statistics that hides most of the mathematical details and computations from the user. Bayesian methods are theoretically grounded in probability theory and hence it's no wonder that many books about Bayesian statistics are full of mathematical formulas requiring a certain level of mathematical sophistication. Learning the mathematical foundations of statistics could certainly help you build better models and gain intuition about problems, models, and results. Nevertheless, libraries, such as PyMC3 allow us to learn and do Bayesian statistics with only a modest mathematical knowledge, as you will be able to verify by yourself throughout this book.

谦虚一个相当谦虚的。安装通过 npm： npm install --save markdown-modest 这个主题与rework-npm很好地集成在一起，并且在 index.css 文件中提供了返工 CSS。克隆回购： git clone https://github.com/markdowncss/modest.git 发展： git clone https://github.com/markdowncss/modest.git && cd modest npm install gulp 用法将 Markdown 中的文件链接到 HTML 构建过程： < link rel =" stylesheet " href =" path/to/css/modest.css " > 执照麻省理工学院贡献分叉吧创建您的功能分支（ git checkout -b

利用JPA做“公共黑板”，解决了数据挖掘中hadoop的子任务无法共享数据的问题，提出了树型结构的高效算法。具体实现了kdtree的hadoop版本。代码可以在http://svn.javaforge.com/svn/hadoopjpa/HadoopDataMining check out. 需要先注册；如果不能成功，换小写地址。下面是ris格式的引文，存盘后可为endnote等文献管理软件导入。 TY - CHAP AU - Lai, Yang AU - ZhongZhi, Shi A2 - Shi, Zhongzhi A2 - Vadera, Sunil A2 - Aamodt, Agnar A2 - Leake, David T1 - An Efficient Data Indexing Approach on Hadoop Using Java Persistence API T2 - Intelligent Information Processing V T3 - IFIP Advances in Information and Communication Technology PY - 2010 PB - Springer Boston SN - SP - 213 EP - 224 VL - 340 UR - http://dx.doi.org/10.1007/978-3-642-16327-2_27 DO - 10.1007/978-3-642-16327-2_27 AB - Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. To resolve problems of globalization, random-write and duration in Hadoop, a data indexing approach on Hadoop using the Java Persistence API (JPA) is elaborated in the implementation of a KD-tree algorithm on Hadoop. An improved intersection algorithm for distributed data indexing on Hadoop is proposed, it performs O(M+logN), and is suitable for occasions of multiple intersections. We compare the data indexing algorithm on open dataset and synthetic dataset in a modest cloud environment. The results show the algorithms are feasible in large-scale data mining. ER -

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