Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained cha

这是一本关于 Fragment Shaders（片段着色器）的入门指南，它将一步一步地带你领略其中的纷繁与抽象, 作者Patricio Gonzalez Vivo和Jen Lowe

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1、使用SQL Server 2000企业管理器和查询分析器工具（即用Transact-SQL语句）创建一个“图书读者数据库”（Book_Reader_DB）； 2、使用企业管理器查看Book_Reader_DB的数据库属性，并进行修改，使之符合你的要求； 3、使用企业管理器和在查询分析器中用Transact-SQL语句的两种方法建立图书、读者和借阅三个表，其结构为： 图书（书号，类别，出版社，作者，书名，定价，备注）； 读者（编号，姓名，单位，性别，电话）； 借阅（书号，读者编号，借阅日期）。 要求：① 对每个属性选择合适的数据类型；② 定义每个表的主码、是否允许空值和默认值等列级数据约束；③ 对每个表的名字和表中属性的名字尽可能用英文符号标识。 4、实现相关约束：①使用企业管理器来建立上述三个表的联系，即实现：借阅表与图书表之间、借阅表与读者表之间的外码约束；② 实现读者性别只能是“男”或“女”的约束。 5、分别用企业管理器和查询分析器修改表的结构。在“图书”表中，增加两个字段，分别为“数量”和“购买日期”。在“借阅”表中增加一个“还书日期”字段。 6、用企业管理器在上述三个表中输入部分虚拟数据。 7、在查询分析器中实现基于但个表的查询 ① select * from Book ② select * from book where Bclass=’计算机’ ③ select count(*) from book group by Bclass ④ select * from Reader ⑤ select * from Borrow ⑥ select rno, count(bno) from Borrow group by rno order by rno ⑦ select bno, count(rno) from Borrow group by bno order by bno

密码故事—人类智力的另类较量（即《The Code Book》的中文版)

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IBM官方英文版powerpc资料，包括Power ISA User Instruction Set Architecture、Power ISA Virtual Environment Architecture、Power ISA Operating Environment Architecture - Server Environment、book E等内容。是学习powerpc不可多得的资料。

The NURBS Book 2nd 完整pdf版图书

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