计算机网络自顶向下方法第五版

《The Little SAS Book 中文版》以大量实例、清晰简明的解释以及尽可能少的术语来介绍SAS语言，且大部分的功能均来自Base SAS。Base SAS包含了所有程序员所使用的核心功能。(The Little SAS Book Chinese Edition introduces SAS language with a large number of examples, clear and concise explanations and as few terms as possible, and most of its functions come from Base SAS. Base SAS contains the core functions used by all programmers.)

流畅的python，mobi+pdf两种格式(fluent python.mobi+pdf two formats)

说明：  visual C ++ ebook for programmer

介绍电烙铁的使用，非常好的资料。值得学习和收场。(introduction of dianlaotie,it very good information of dianlaotie. useful to study and get.)

中国卫生信息数据元值域代码,卫生信息数据元值域代码.(Range of health information data element code range of health information data element code)

该文档详细讲解了路由器的配置过程，是一个学习路由器配置的很好的学习资料(The document gave a detailed router configuration process is a learning router configuration a good learning materials)

说明：  中国海洋大学物理海洋学笔记 采用Latex编写(Physical Oceanography Notes Latex)

采用Matlab GUI设计对光学实验进行仿真，界面丰富直观，可以直接在界面中输入和改变参数而不需要改变原程序，直观地分析各参数的变化对实验结果的影响。为光学理论分析与实验教学提供了方便，为相关课件的设计提供了新的途径。(The GUI(Graphical User Interfaces)is beautiful and briefly，the user can directly input the parameters on the GUI，but not alter the primary procedure，making the user able to analyze the parameters influencing the result．It makes it easy to teach optical theory and experiment and offers a new way to design the related CAI． Key words：Matlab GUI；optical experiment；simulation)

薛培峰用FVCOM模式模拟长江盐水入侵的案例，很不错，很经典(Xue Peifeng FVCOM simulated by salt water intrusion in the case of the Yangtze River, is very good, very classic)

关于关联向量机应用的最新文献！ 提出了一种核主元分析(KPCA)和关联向量机(RVM)相结合的组合建模方法。KPCA-RVM采用KPCA对原始自变量进行非线性变换并提取主成分,形成特征自变量 采用RVM,对KPCA变换后的样本数据进行回归建模,并根据模型的预报能力自适应的确定参与回归的最佳特征变量个数,消除冗余信息干扰,获得强非线性表达能力且预报性能良好的模型。并将KPCA-RVM应用于PTA装置对羧基苯甲醛(4-CBA)含量的软测量建模,结果表明该方法预测精度高于PCA-RVM和RVM。(A novel modeling method integrated KPCA with RVM was proposed. The kernel primary component analysis (KPCA) was employed to identify the principal components from the nonlinear transform data of independent variables, which were regarded as character variables. Regression between character variables and dependent variables was done based on RVM, and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM meth...)