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keynote:2011-lesson03 [2011/06/26 06:16]
11021015 [第三课]
keynote:2011-lesson03 [2011/06/26 06:17]
11021015 [第三课]
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                      * Although it can be proved that the procedure will always terminate, the k-means algorithm does not necessarily find the most optimal configuration,​ corresponding to the global objective function minimum. The algorithm is also significantly sensitive to the initial randomly selected cluster centres. The k-means algorithm can be run multiple times to reduce this effect.                      * Although it can be proved that the procedure will always terminate, the k-means algorithm does not necessarily find the most optimal configuration,​ corresponding to the global objective function minimum. The algorithm is also significantly sensitive to the initial randomly selected cluster centres. The k-means algorithm can be run multiple times to reduce this effect.
                      * K-means is a simple algorithm that has been adapted to many problem domains. As we are going to see, it is a good candidate for extension to work with fuzzy feature vectors. ​                      * K-means is a simple algorithm that has been adapted to many problem domains. As we are going to see, it is a good candidate for extension to work with fuzzy feature vectors. ​
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        * 高斯混合模型 Gaussian Mixture Model        * 高斯混合模型 Gaussian Mixture Model
                      * 如果 $p(x | \theta_m)$ 是多元正太分布,即高斯分布,则此混合聚类的模型即为高斯混合模型(GMM)。在高斯混合模型中,$\theta_m = {\{\mu_m, \sum_m}\}$,其中$\mu_m$表示第m个成分的均值,$\sum_m$表示第m个成分的协方差。其中,概率密度函数可以表示为:                      * 如果 $p(x | \theta_m)$ 是多元正太分布,即高斯分布,则此混合聚类的模型即为高斯混合模型(GMM)。在高斯混合模型中,$\theta_m = {\{\mu_m, \sum_m}\}$,其中$\mu_m$表示第m个成分的均值,$\sum_m$表示第m个成分的协方差。其中,概率密度函数可以表示为:
keynote/2011-lesson03.txt · Last modified: 2014/05/22 08:34 (external edit)