auto-Machine-Learning-Methods-Sys

This book is good for learning C++.It conclude five parts:professional c++ introduction,C++ codeing

This book is good for learning C++.It conclude five parts:professional c++ introduction,C++ codeing the professional way,mastering advanced features of c++, ensuring Bug-free code and using libraries and patterns.

标签： introduction professional conclude learning

上传时间： 2016-01-06

上传用户：ljmwh2000
mirror 驱动之 sys 部分

mirror 驱动之 sys 部分，比DDK上的架构多出了一点自己的东西。供参考。

标签： mirror sys 驱动分

上传时间： 2013-12-11

上传用户：shus521
《Learning C++ as a new language》is a very good book.

《Learning C++ as a new language》is a very good book.

标签： Learning language good book

上传时间： 2013-12-23

上传用户：363186
This forced me to write about more interesting and comprehensive sorting methods, the result of whic

This forced me to write about more interesting and comprehensive sorting methods, the result of which is this one. Through this writing I have tried to give in-depth coverage of the entire sort algorithm I hope Peter wouldn t mind reading it. This article assumes that you really don t know about the iterations, looping, and so forth hence, it explains these in detail first.

标签： comprehensive interesting methods sorting

上传时间： 2016-01-10

上传用户：athjac
此文档是AUTO CAD的dxf文件格式的配置文件

此文档是AUTO CAD的dxf文件格式的配置文件，适合在linux上用C语言开发.

标签： AUTO CAD dxf 文档

上传时间： 2016-01-13

上传用户：叶山豪
Stroustrup s Guide To Learning C

Stroustrup s Guide To Learning C

标签： Stroustrup Learning Guide To

上传时间： 2016-01-17

上传用户：康郎
usbvideo.sys usbvideo.sys usbvideo.sys usbvideo.sys

usbvideo.sys usbvideo.sys usbvideo.sys usbvideo.sys

标签： usbvideo sys

上传时间： 2016-01-18

上传用户：wyc199288
open bsd stand lib of sys

open bsd stand lib of sys

标签： stand open bsd lib

上传时间： 2016-01-21

上传用户：上善若水
CNC Machine Router Table Design with Specs

CNC Machine Router Table Design with Specs

标签： Machine Design Router Table

上传时间： 2013-12-23

上传用户：klin3139
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form

Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.

标签： meta-learning classifiers combining Boosting

上传时间： 2016-01-30

上传用户：songnanhua