Informix-backup-restore-manual

wxPythonInAction一书及其源代码，内容详尽(The book of wxPythonInAction,with its original codes,being very well detailed)

北京大学地球物理反演课程的一些资料，很有用，讲一些地球物理反演的理论和方法(Geophysical inversion)

说明：  C8051F930完整详细的中文资料，包括9XX系列的管脚定义，封装、应用电路等(C8051F930 complete details of the Chinese data, including the 9XX series of pin definition, package, application circuit, etc.)

非常好的资料，比较详细，是一个体系，希望对大家有帮助C++面试题(Very good information, more detailed, is a system, we hope to help C++ face questions)

说明：  python 关于深度学习以及Imageai的一些资料(Python deep learning and other file about the Imageai)

条件随机场ppt，建议先了解一些基础知识(CRFs ppt, it is recommended to learn some of the basics)

ICL7660是一款不错的正压变负压的稳压芯片，这里提供中英文资料各一份(ICL7660 is a good positive change negative regulator chip, the information provided here in English A)

Python配套使用资料，更直观的看到协议分析图，更好地学习。(Python supporting the use of data, see the protocol analysis more intuitive, better learning. )

说明：  用于进行色彩空间转换的外文资料，有做相关方面的可以看看(Used for color space conversion of foreign materials, there can look at relevant aspects of doing)

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...)