SVM分类-每个类别的最小输入集数 [英] SVM Classification - minimum number of input sets for each class

问题描述

我正在尝试构建一个应用程序来检测图像，这些图像是来自网页的广告.一旦检测到这些内容，我将不允许它们在客户端显示.

I'm trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.

在我对这个Stackoverflow问题的帮助下，我认为支持向量机是实现我目标的最佳方法.

From the help that I got on this Stackoverflow question, I thought SVM is the best approach to my aim.

因此，我已经为SVM和SMO编写了代码.我从UCI数据存储库中获得的数据集具有3280个实例(链接到数据集)，其中大约有400种来自代表广告图像的类，其余代表非广告图像.

So, I have coded SVM and an SMO myself. The dataset which I have got from UCI data repository has 3280 instances ( Link to Dataset ) where around 400 of them are from class representing Advertisement images and rest of them representing non-advertisement images.

现在，我正在使用第一个2800个输入集并训练SVM.但是，在查看了准确率之后，我意识到这2800个输入集中的大多数来自非广告图像类.因此，我对该课程有很好的准确性.

Right now I'm taking the first 2800 input sets and training the SVM. But after looking at the accuracy rate I realised that most of those 2800 input sets are from non-advertisement image class. So I`m getting very good accuracy for that class.

那我该怎么办?我应该给SVM训练多少输入集，每个班级有多少输入集?

So what can I do here? About how many input set shall I give to SVM to train and how many of them for each class?

谢谢.干杯. (基本上提出了一个新问题，因为上下文与我之前的问题有所不同.神经网络的优化输入数据)

Thanks. Cheers. ( Basically made a new question because the context was different from my previous question. Optimization of Neural Network input data )

感谢您的回复. 我想检查是否正确推导广告类和非广告类的C值. 请给我反馈.

Thanks for the reply. I want to check whether I`m deriving the C values for ad and non-ad class correctly or not. Please give me feedback on this.

或者您可以在此处看到文档版本.

Or you u can see the doc version here.

您可以在此处看到y1等于y2的图表

You can see graph of y1 eqaul to y2 here

，此处的y1不等于y2

and y1 not equal to y2 here

推荐答案

有两种解决方法.一种方法是平衡训练数据，使其包含相等数量的广告和非广告图像.这可以通过对400个广告图像进行过采样或对数千个非广告图像进行欠采样来完成.由于训练时间会随着使用的数据点数量而急剧增加，因此您可能应该首先尝试对非广告图像进行欠采样，然后使用400个广告图像和400个随机选择的非广告创建训练集.

There are two ways of going about this. One would be to balance the training data so it includes an equal number of advertisement and non-advertisement images. This could be done by either oversampling the 400 advertisement images or undersampling the thousands of non-advertisement images. Since training time can increase dramatically with the number of data points used, you should probably first try undersampling the non-advertisement images and create a training set with the 400 ad images and 400 randomly selected non-advertisements.

另一种解决方案是使用加权SVM ，以使广告图像的边距误差比非广告的边距误差具有更大的权重，对于libSVM软件包，这是通过标志.根据对数据的描述，您可以尝试对广告图像进行加权，其权重约为非广告图像的7倍.

The other solution would be to use a weighted SVM so that margin errors for the ad images are weighted more heavily than those for non-ads, for the package libSVM this is done with the -wi flag. From your description of the data, you could try weighing the ad images about 7 times more heavily than the non-ads.

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