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Bayse

于 2021-01-05 发布

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下载积分: 1 下载次数: 2

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说明：贝叶斯方法将所有参数看作随机变量，将未知参数的先验信息与观测数据综合，再由贝叶斯定理求算出后验概率分布，进而根据后验分布去推断未知参数。(Bayesian method regards all parameters as random variables, synthesizes the prior information of unknown parameters with observation data, calculates the posterior probability distribution by Bayesian theorem, and infers unknown parameters according to the posterior distribution.)

文件列表：

Bayse, 0 , 2021-01-05
Bayse\A.xls, 20480 , 2020-12-22
Bayse\B.xls, 19968 , 2020-12-22
Bayse\bayse.m, 1282 , 2021-01-04
Bayse\C.xls, 19968 , 2020-12-22
Bayse\EF_RGL.m, 101 , 2020-12-22
Bayse\junyunfenbu.m, 205 , 2020-12-28
Bayse\lieklihood.m, 165 , 2020-12-28
Bayse\main_bayse.m, 748 , 2020-12-28
Bayse\NS_CAL.m, 101 , 2020-07-25
Bayse\runfall.xls, 19968 , 2020-12-28
Bayse\x1.xls, 19968 , 2020-12-22
Bayse\x2.xls, 19968 , 2020-12-22
Bayse\贝叶斯优化答案.xlsx, 137742 , 2021-01-04
Bayse\输入数据.xlsx, 28886 , 2020-12-29

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2013-11-29 17:18:47下载

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用adaboost算法生成基支持向量机分类器，并对识别结果进行简单投票法集成。附有支持向量机工具箱和adaboost算法流程说明。(Adaboost algorithm to generate the base with a support vector machine classifier, and the recognition result is a simple voting method integration. With support vector machine algorithm toolbox and adaboost process description.)

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