在我的分类问题中ANN，SVM和KNN之间的区别 [英] what the difference among ANN, SVM and KNN in my classification question

问题描述

我知道问题是这么大，如果没有针对实际项目。对于我的问题：我只是做遥感图像分类，我使用面向对象的方法：首先我将图像分割到不同的区域，然后我从区域提取特征，如颜色，形状和纹理。区域中的特征可以是30，并且通常总共有2000个区域，并且将选择5个类，每个类具有15个样本。所以总之：采样数据1530;测试数据197530;现在我面对如何选择合适的分类器来实现分类，如果有3个分类器：ANN，SVM，KNN。我应该选择更好的分类吗？

i know the question is so big if without aiming for actual project . for my question : i just do the remote sensing image classification , i use 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 .and the number of all features in a region may 30， and commonly there are 2000 regions in all ,and i will choose 5 classes with 15 samples for every class. so in sum : sample data 1530 ; test data 197530; now i faced how to choose the proper classifier to implement the classification , if there are 3 classifier : ANN ,SVM,KNN .which should i choose for the better classification ?

推荐答案

如果你的样本数据是火车集，它似乎很小。我首先建议每个类使用超过15个示例。

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

正如评论中所说，最好是将算法与问题相匹配，这样您可以简单地测试看看哪个算法工作得更好。但首先，我建议SVM：它比KNN小火车套件更好，一般更容易训练，然后ANN，因为有更少的选择。

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.

这篇关于在我的分类问题中ANN，SVM和KNN之间的区别的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！