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Description: Parzen 窗 和 K近邻法进行概率密度估计 还带一个示波器控件.-Parzen window and K-nearest neighbor method probability density is estimated to bring an oscilloscope control.
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Size: 51200 |
Author: 肖龙远 |
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Description: This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox
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Size: 4047872 |
Author: wsy |
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Description: ddtool,实现one class classification.包括gaussian 模型， gaussian 混合模型，Parzen density，knn,kmean,kcenter等方法-ddtool, the realization of one class classification. including the Gaussian model, gaussian mixture model, Parzen density, knn, kmean, kcenter methods such as
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Size: 439296 |
Author: 何威 |
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Description: K最邻近密度估计分类,K最邻近密度估计技术是一种分类方法，不是聚类方法。-K nearest neighbor classification density estimation, K nearest neighbor density estimation technique is a classification method, not the clustering method.
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Size: 1024 |
Author: 施宇君 |
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Description: K近邻（KNN）：分类算法KNN是non-parametric分类器（不做分布形式的假设，直接从数据估计概率密度），是memory-based learning KNN不适用于高维数据（curse of dimension）-K-Nearest Neighbor (KNN): Classification Algorithm. KNN is a non-parametric classifiers (not to assume that the distribution of forms, from direct estimation of the probability density data), a memory-based learning.
* KNN does not apply to high-dimensional data (curse of dimension)
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Size: 1024 |
Author: 王海 |
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Description: K最邻近密度估计技术是一种分类方法，不是聚类方法。
不是最优方法，实践中比较流行。
通俗但不一定易懂的规则是：
1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。
2.选出最小的前K数据个距离，这里用到选择排序法。
3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method.
It is not the best method, but it is popular in practice.
Popular but not necessarily understandable rule is:
1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov).
2. select the minimum distance from the previous K data, where the choice sorting method is used.
3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)
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Size: 1024 |
Author: 晓骸
|
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Description: K最邻近密度估计技术是一种分类方法，不是聚类方法。
不是最优方法，实践中比较流行。
通俗但不一定易懂的规则是：
1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。
2.选出最小的前K数据个距离，这里用到选择排序法。
3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method.
It is not the best method, but it is popular in practice.
Popular but not necessarily understandable rule is:
1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov).
2. select the minimum distance from the previous K data, where the choice sorting method is used.
3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)
Platform: |
Size: 1024 |
Author: 咳晓
|
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