KVM (for Kernel-based Virtual Machine) is a full virtualization solution for Linux on x86 hardware. It consists of a loadable kernel module (kvm.ko) and a userspace component. Using KVM, one can run multiple virtual machines running unmodified Linux or Windows images. Each virtual machine has private virtualized hardware: a network card, disk, graphics adapter, etc. The kernel component of KVM is included in mainline Linux, and will appear in Linux 2.6.20. KVM is open source software.
標簽： virtualization Kernel-based for hardware
上傳時間： 2015-08-20
上傳用戶：lijianyu172
machine vision calibration
標簽： calibration machine vision
上傳時間： 2015-08-20
上傳用戶：gyq
a chap 2 for learn ing about java, it is a good for begineer start from learning for java
標簽： for java begineer learning
上傳時間： 2014-01-10
上傳用戶：evil
a chap 315 updated for learn ing about java, it is a good for begineer start from learning for java
標簽： for java begineer learning
上傳時間： 2015-08-23
上傳用戶：思琦琦
《java virtual machine》是研究jvm的一本書，由臺灣的蔡學(xué)庸翻譯，這是例子源碼
上傳時間： 2015-08-27
上傳用戶：源弋弋
SVM, Support Vector Machine 支持向量機程序
標簽： Machine Support Vector SVM
上傳時間： 2014-01-12
上傳用戶：rocketrevenge
This is the machine-generated representation of a Handle Graphics object and its children. Note that handle values may change when these objects are re-created. This may cause problems with any callbacks written to depend on the value of the handle at the time the object was saved.
標簽： machine-generated representation Graphics children
上傳時間： 2013-12-18
上傳用戶：miaochun888
U盤(auto病毒)類病毒分析與解決方案,asd
上傳時間： 2015-08-28
上傳用戶：heart520beat
The third edition of Learning GNU Emacs describes Emacs 21.3 from the ground up, including new user interface features such as an icon-based toolbar and an interactive interface to Emacs customization. A new chapter details how to install and run Emacs on Mac OS X, Windows, and Linux, including tips for using Emacs effectively on those platforms.
標簽： Emacs describes including Learning
上傳時間： 2015-08-29
上傳用戶：caixiaoxu26
LVQ算法( Learning Vector Quantization，學(xué)習(xí)矢量量化網(wǎng)絡(luò))是一種基于模型（神經(jīng)網(wǎng)絡(luò)）的方法，本實驗要實現(xiàn)的是對LVQ改進的聚類方法——MLVQ（閆德勤等人提出）。該方法克服了LVQ算法對初值敏感的問題和廣義學(xué)習(xí)矢量量化(GLVQ)網(wǎng)絡(luò)算法性能不穩(wěn)定的缺點。(附文章)
標簽： Quantization Learning Vector LVQ
上傳時間： 2015-08-31
上傳用戶：youke111
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