RFID二次调制信号的I、Q相关解调模型，直接运行即可
上传时间： 2016-01-12
上传用户：rocwangdp
测试人员及测试在项目开发中的角色 测试生命周期及主要的活动 测试用例编写的方法及一般准则 思考和Q＆A
上传时间： 2016-01-15
上传用户：hj_18
将军——一种新的求解大规模问题的支持向量机程序（软件）。A Large Scale Machine Learning Toolbox
标签： Learning Machine Toolbox Large
上传时间： 2013-12-14
上传用户：zq70996813
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
用FFT分别计算Xa(n) (p=8, q=2)与Xb(n) (a =0.1,f =0.0625)的16点循环卷积和线性卷积。
上传时间： 2013-12-09
上传用户：lizhizheng88
单纯形法算法,int K,M,N,Q=100,Type,Get,Let,Et,Code[50],XB[50],IA,IAA[50],Indexg,Indexl,Indexe float Sum,A[50][50],B[50],C[50]
上传时间： 2013-12-22
上传用户：顶得柱
gve reg wtytu wttr yqrtg qrtfq34 rt432rt q rqet 3q4
上传时间： 2016-01-27
上传用户：zhichenglu
booster-tree a machine learning method
标签： booster-tree learning machine method
上传时间： 2014-06-09
上传用户：龙飞艇
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
using Monte Carlo integeration calculate Q function
标签： integeration calculate function using
上传时间： 2013-12-25
上传用户：曹云鹏
