kernel-devel-3.10.0-1160.el7.x86_64.rpm centos7的对应内核开发工具，装显卡驱动会用到

linux协议栈源码，带注释版本，是学习linux tcp/ip协议栈的最佳伴侣。 linux内核版本2.6

One of the most exciting recent developments in machine learning is the discovery and elaboration of kernel methods for classification and regression. These algorithms combine three important ideas into a very successful whole. From mathematical programming, they exploit quadratic programming algorithms for convex optimization; from mathematical analysis, they borrow the idea of kernel representations; and from machine learning theory, they adopt the objective of finding the maximum-margin classifier. After the initial development of support vector machines, there has been an explosion of kernel-based methods. Ralf Herbrich’s Learning Kernel Classifiers is an authoritative treatment of support vector machines and related kernel classification and regression methods. The book examines these methods both from an algorithmic perspective and from the point of view of learning theory. The book’s extensive appendices provide pseudo-code for all of the algorithms and proofs for all of the theoretical results. The outcome is a volume that will be a valuable classroom textbook as well as a reference for researchers in this exciting area. The goal of building systems that can adapt to their environment and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. Recently, several research communities have begun to converge on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. TheMIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high quality research and innovative applications. Thomas Dietterich

KPCA，有些复杂，但很详细

AODV协议文件是rfc3561.mht kernel AODV实现是kernel-aodv_v2.2.2.tgz ，linux操作系统 UoBWinAODV Windows 实现UoBWinAODV-0.1.zip，在Windows下实现。 AODV FOR IPv6在文件夹中

模式分析的核方法 英文原版。 Pattern Analysis is the process of finding general relations in a set of data, and forms the core of many disciplines, from neural networks to so-called syn- tactical pattern recognition, from statistical pattern recognition to machine learning and data mining. Applications of pattern analysis range from bioin- formatics to document retrieval.

本篇文章主要基于python语言和OpenCV库（cv2）进行车牌区域识别和字符分割，开篇之前针对在python中安装opencv的环境这里不做介绍，可以自行安装配置！ 车牌号检测需要大致分为四个部分： 1.车辆图像获取 2.车牌定位、 3.车牌字符分割 4.车牌字符识别 具体介绍 车牌定位需要用到的是图片二值化为黑白后进canny边缘检测后多次进行开运算与闭运算用于消除小块的区域，保留大块的区域，后用cv2.rectangle选取矩形框，从而定位车牌位置 车牌字符的分割前需要准备的是只保留车牌部分，将其他部分均变为黑色背景。这里我采用cv2.grabCut方法，可将图像分割成前景与背景。分割

详细描述如何使用gcov工具来进行内核的代码覆盖率测试。包括linux-2.4内核和linux-2.6内核。极度推荐！

kernel代码

linux-4.4的内核源码！ 配置内核配置内核的方法很多，主要有如下几种：1.      #make menuconfig  //基于ncurse库编制的图形工具界面2.      #make config  //基于文本命令行工具，不推荐使用3.      #make xconfig  //基于X11图形工具界面4.      #make gconfig  //基于gtk+的图形工具界面由于对Linux还处在初学阶段，所以选择了简单的配置内核方法，即make menuconfig。在终端输入make menuconfig，等待几秒后，终端变成图形化的内核配置界面。进行配置时，大部分选项使用其缺省值，只有一小部分需要根据不同的需要选择。对每一个配置选项，用户有三种选择，它们分别代表的含义如下：或[*]——将该功能编译进内核[]——不将该功能编译进内核[M]——将该功能编译成可以在需要时动态插入到内核中的代码说明：笔者输入make menuconfig  后并没有配置其他内核编译配置（因为还不是特别懂），只是试了下此流程，是可用的。4)    编译内核这步是时间最长的一个步骤，一般在2个小时左右。编译内核只需在终端(目录：/usr/src/linux-4.14) 输入:make