ImageNet classification with deep convolutional neural networks 中文翻译

将下载的dll压缩包中的dll全部放到C盘的windows目录下就可以了 压缩包包含五个dll，只要全部放到windows目录下就好了 为解决以下异常： 运行时出错! 错误信息：无法找到指定DLL库文件“classification_dll.dll”中的输出命令“createTaskPoolByData”

Classification matlab code - Quadratic Discriminant Analysis

图像识别与分类的算法系统应用的论文集 This book presents important recent advances in sensors, image processing algorithms, and systems for image recognition and classification with diverse applications in military, aerospace, security, image tracking, radar, biomedical, and intelligent transportation. The book includes contributions by some of the leading researchers in the field to present an overview of advances in image recognition and classification over the past decade. It provides both theoretical and practical information on advances in the field.

《模式分类》高清英文原版，经典教材，可以用作手边的工具书，供大家学习使用

这是一本关于ENVI IDL软件很好的国外培训教材，可供遥感图像处理的学着使用！

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.

RBM-on-Classification，用RBM所做的分类，里面包含源码和数据集，独立于任何工具箱，整个就是一个工程，里面有仿真和图像，还有各种有用的数据函数

答案为英文版，有目录，总共有446页，包含全部十个章节的答案以及代码。

利用GlobalMapper软件对LiDAR点云数据进行分类的流程图。