Network Economics: A VARIATIONAL INEQUALITY APPROACH （by Anna_Nagurney)

An Introduction to MultivariateStatistical Analysis (3ed)

讲述贝叶斯变分法在信号处理中的应用,极具参考价值!

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems. This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

光谱的变量选择/特征选择算法

Posterior Regularization for Structured Latent Variable Models 一文里第二章的阅读笔记，用中文记录下自己的一些认知

国外经典统计分析教材，讲述多变量统计的详细理论分析

整理的Cacti1.2版本一键安装脚本，包括PHP7和Spine、Mariadb。在CentOS7下，先执行 install.sh 安装，推荐安装1.2版本 按提示输入数据库 root密码、cacti密码、本机SNMP团体字即可。登录web（http://ip/cacti) 完成继续安装。

Learning Generative Adversarial Networks_Next generation deep learning simplified First published: October 2017 | 203页 | pdf格式

复旦大学_软件安全_SEED labs_3-Environment_Variable_and_SetUID实验 是从雪城大学SEED labs上找的实验 资源包括：实验报告详细版、实验指导书、参考链接