神经网络输入数据的优化 [英] Optimization of Neural Network input data

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.

基本上，我正在使用反向传播算法来训练神经网络，并使用此处提供的数据集: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements .

Basically I'm using Back-propagation algorithm to train the neural network using the dataset given here: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements.

但是在那个数据集中没有.的属性很高.实际上，该项目的一位导师告诉我，如果您训练具有这么多属性的神经网络，将需要很多时间来进行训练.那么有没有一种方法可以优化输入数据集?还是我只需要使用那么多属性?

But in that dataset no. of attributes are very high. In fact one of the mentors of the project told me that If you train the Neural Network with that many attributes, it'll take lots of time to get trained. So is there a way to optimize the input dataset? Or I just have to use that many attributes?

推荐答案

1558实际上是适量的功能/属性.实例数(3279)也很小.问题不在于数据集，而在于训练算法.

1558 is actually a modest number of features/attributes. The # of instances(3279) is also small. The problem is not on the dataset side, but on the training algorithm side.

ANN的训练速度很慢，建议您使用逻辑回归或svm.他们两个都非常快地训练.特别是，svm具有许多快速算法.

ANN is slow in training, I'd suggest you to use a logistic regression or svm. Both of them are very fast to train. Especially, svm has a lot of fast algorithms.

在此数据集中，您实际上是在分析文本，而不是图像.我认为线性家庭分类器(即逻辑回归或svm)更适合您的工作.

In this dataset, you are actually analyzing text, but not image. I think a linear family classifier, i.e. logistic regression or svm, is better for your job.

如果用于生产并且不能使用开源代码.与良好的ANN和SVM相比，逻辑回归非常容易实现.

If you are using for production and you cannot use open source code. Logistic regression is very easy to implement compared to a good ANN and SVM.

如果您决定使用逻辑回归或SVM，我将来会推荐一些文章或源代码供您参考.

If you decide to use logistic regression or SVM, I can future recommend some articles or source code for you to refer.

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