CentOS 7内核rpm包kernel-headers-3.10.0-1127.19.1.el7.x86_64.rpm

前言：这个文件的源码已经在博客里公开了，https://blog.csdn.net/yujianliam/article/details/115866832。 1.内容：测试环境的简单module

非转换版本，是原版pdf，排版很好

该视频讲述了linux下内核的设计，有助于新手理解。

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.