SVM 分类 - 每个类别的最小输入集数量
我正在尝试构建一个应用程序来检测来自网页的广告图像。一旦我检测到这些，我将不允许它们显示在客户端。
从我在这个 Stackoverflow 问题获得的帮助中，我认为 SVM 是实现我的目标的最佳方法。
所以，我自己编写了 SVM 和 SMO。我从 UCI 数据存储库获得的数据集有 3280 个实例（ 链接到数据集< /a> ）其中大约 400 个来自表示广告图像的类，其余的表示非广告图像。
现在我正在获取前 2800 个输入集并训练 SVM。但在查看准确率后，我意识到这 2800 个输入集中的大多数都来自非广告图像类别。所以我在那堂课上获得了非常好的准确性。
那么我在这里能做什么呢？我应该给 SVM 多少个输入集来训练，每个类别有多少个？
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不同。神经网络的优化输入数据）
感谢您的回复。 我想检查我是否正确导出广告和非广告类的 C 值。 请就此向我提供反馈。
或者您可以查看文档版本 此处。
您可以在此处查看 y1 等于 y2 的图表 
且此处 y1 不等于 y2 
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.
From the help that I got on this Stackoverflow question, I thought SVM is the best approach to my aim.
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.
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.
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?
Thanks. Cheers. ( Basically made a new question because the context was different from my previous question. Optimization of Neural Network input data )
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.
Or you u can see the doc version here.
You can see graph of y1 eqaul to y2 here
and y1 not equal to y2 here
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有两种方法可以解决这个问题。一种方法是平衡训练数据，使其包含相同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的增加而急剧增加，因此您可能应该首先尝试对非广告图像进行欠采样，并使用 400 个广告图像和 400 个随机选择的非广告创建一个训练集。
另一种解决方案是使用加权 SVM，以便广告图像的边距误差比非广告的边距误差权重更大，对于 libSVM 包，这是通过
-wi 完成的标志。根据您对数据的描述，您可以尝试将广告图像的重量比非广告图像的重量增加约 7 倍。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.
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
-wiflag. From your description of the data, you could try weighing the ad images about 7 times more heavily than the non-ads.训练集所需的大小取决于特征空间的稀疏程度。据我所知，您没有讨论您选择使用哪些图像功能。在训练之前，您需要将每个图像转换为描述图像的数字（特征）向量，希望能够捕获您关心的方面。
哦，除非你为了运动而重新实现 SVM，否则我建议只使用 libsvm ,
The required size of your training set depends on the sparseness of the feature space. As far as I can see, you are not discussing what image features you have chose to use. Before you can train, you need to to convert each image into a vector of numbers (features) that describe the image, hopefully capturing the aspects that you care about.
Oh, and unless you are reimplementing SVM for sport, I'd recomment just using libsvm,