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RankBoost
RankBoost算法实现,有dll文件。代码为matlab.(rankboost algorithm code implementation.)
- 2009-03-30 08:05:39下载
- 积分:1
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Emprendedores-Mexicanos
guia de emprendedores(empesar una pyme)
- 2014-11-03 07:42:44下载
- 积分:1
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Email_Free
说明: 自动发送邮件,使用该程序可以实现邮件自动发送(Send mail automatically)
- 2020-06-22 19:20:01下载
- 积分:1
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Ricker-wavelet
雷克子波的一套完整的详细的matlab程序,确实有问题,不要下载,我已经修正,并已经上传(Ricker wave matlab a complete set of detailed procedures)
- 2011-04-20 09:27:53下载
- 积分:1
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Many-sound-signals
matlab 多音信号的产生 多音信号的产生(Many sound signals)
- 2013-03-20 10:01:37下载
- 积分:1
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INS_OUT
惯性导航的分针分析,可以模拟出不同观测角,不同方向的速度(The minute hand of the inertial navigation analysis, we can simulate different viewing angle, the speed of different directions)
- 2012-07-09 19:46:08下载
- 积分:1
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Fourier-Transforms-with-MATLAB
Fourier Transforms with MATLAB
- 2015-03-23 22:25:15下载
- 积分:1
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butterfly-option-strategy
期权蝶式价差策略:把价差策略分解成单个期权的组合并分析期初成本和期末收益(Butterfly spread options strategy: to spread down into a combination of a single policy options and cost analysis at the beginning and end of the income)
- 2014-09-11 10:42:26下载
- 积分:1
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gaosi
高斯-赛德尔迭代法求解方程组,通过matlab编程实现。(Gauss-seidel iterative method for solving equations, by matlab programming.
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- 2014-01-10 15:28:51下载
- 积分:1
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gender-classification-experiments
这是用身高体重数据进行性别分类的实验。
用最小错误率贝叶斯分类器决策时,首先通过比较概率大小判断一个体重身高二维向量代表的人是男是女,然后再逐一与已知性别的数据比较,就可以得到错误率的统计。然后改变先验概率,重复上面的过程,观察数据结果的变化。
用最小风险贝叶斯分类器决策时,首先求出用最小错误率贝叶斯分类器得到的条件概率;然后根据人为给定的决策表,根据公式算出条件风险;然后逐一比较条件风险,找出使条件风险最小的决策(也就是分类)。最后用分类得到的结果逐一比较已经知道的原始数据,统计处错误率。
(This is the height and weight data for gender classification experiment.
With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data .
Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate .
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- 2012-02-02 20:40:46下载
- 积分:1