ANN，SVM和KNN分类器之间有什么区别？ [英] What's the difference between ANN, SVM and KNN classifiers?

问题描述

我知道这是一个非常普遍的问题，没有关于我的实际项目的细节，但我的问题是：

I know this is a very general question without specifics about my actual project, but my question is:

我正在进行遥感图像分类。我正在使用面向对象的方法：首先我将图像分割到不同的区域，然后从颜色，形状和纹理等区域中提取特征。一个地区的所有功能的数量可能是30个，通常总共有2000个地区，我会选择5个课程，每个课程有15个样本。

I am doing remote sensing image classification. I am using the object-oriented method: first I segmented the image to different regions, then I extract the features from regions such as color, shape and texture. The number of all features in a region may be 30 and commonly there are 2000 regions in all, and I will choose 5 classes with 15 samples for every class.

总结：

• 样本数据1530


• 测试数据197530

如何选择合适的分类器？如果有3个分类器（ANN，SVM和KNN），我应该选择哪个更好的分类？

How do I choose the proper classifier? If there are 3 classifiers (ANN, SVM, and KNN), which should I choose for better classification?

推荐答案

如果你的样本数据是列车集，看起来很小。我首先建议每个类使用超过15个例子。

If your "sample data" is the train set, it seems very small. I'd first suggest using more than 15 examples per class.

如评论中所述，最好将算法与问题相匹配，这样你就可以简单地测试到看哪种算法效果更好。但首先，我建议SVM：它比小型列车组的KNN更好，并且通常更容易训练ANN，因为可以做的选择较少。

As said in the comments, it's best to match the algorithm to the problem, so you can simply test to see which algorithm works better. But to start with, I'd suggest SVM: it works better than KNN with small train sets, and generally easier to train then ANN, as there are less choices to make.

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