Description: 本代码是实现支持向量机的源码，用VC++实现，如果不懂请读里面的文件说明-This code is to achieve source Support Vector Machine with VC++ Achieve, if not understand, please read the documentation inside Platform: |
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Author:王刚 |
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Description: matlab 神经网络算法应用于图片中人物性格识别,通过对神经网络的训练，可以识别图像中是男性还是女性-matlab neural network algorithm is applied to the picture of character recognition, neural network through training, can identify the image is male or female. . . Platform: |
Size: 638976 |
Author:Turing |
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Description: a systematic study on gender classification with
automatically detected and aligned faces Platform: |
Size: 2146304 |
Author:Alarmmy |
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Description: Human face contains a variety of information for adaptive social interactions amongst people. In fact, individuals are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, such as gender, ethnicity, and age. In particular, recognizing human gender is important since people respond differently according to gender. In addition, a successful gender classification approach can boost the performance of many other applications, including person recognition and smart human-computer interfaces. Platform: |
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Author:mhm |
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Description: 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 .
Platform: |
Size: 90112 |
Author:崔杉 |
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