Question

我正在使用SVM来预测一辆公共汽车到达途中的行车时间。在进行了大量研究和阅读一些论文之后,我仍然对如何模仿我的系统有关键怀疑。

We ve decided which features to use and we are in the process of gathering the data required to perform the regression, but what is confusing us are the implications or consequences of using some features as input for the SVM or building separated machines based on some of these features.

For instance, in this paper the authors built 4 SVMs for predicting bus arrival times: one for rush hour on sunny days, rush hour on rainy days, off-rush hour on sunny days and the last one for off-rush hours and rainy days.

但是,在

何时选择其中一种方法?

Answer 1

Most important question: what is the data like? Second question: what model do you expect to capture this?

因此,如果你想出于某种原因使用SVERS,就会铭记其基本机制是线性的,而且如果数据由适当的方位转换,只能捕获非线性的现象。

For a particular problem at hand that means:

Do you have reason (plots, insights in the problem nature) to believe your problem is linear(ly separable)? Just use one linear svm.
Do you have reason your problem consist of several linear subproblems? Use a linear svm on each of the subproblems.
Does your data seem non-linearly grouped? Try an svm with something like rbf kernel.

当然,你只能pl弄和尝试,但检查上述情况可能会加深对这个问题的了解。

Answer 2

In your particular problem I would go for single SVM.

由于我没有这么广泛的经验,我将考虑以以下原因在几个小型和小型设备中消除一个问题:

The classes are too different, or there are classes and subclasses in your problem. E.g. in my case: there are several types of antibodies in a microscope image and they all may be positive or negative. So instead of defining A_Pos, A_Neg, B_Pos, B_Neg, ... I decide first if the image is positive or negative and determine the type in second SVM.
采物价格太高。如果你有几类班级,可以发现这些班级有点。您不是为单一机器提取所有特征,而是首先可以提取一个小小子,如果需要(但可能性不大),可以提取更多的特征。
裁定案件是否属于问题。形成一个包含一门课程和所有培训类型的模式。如果要分类的案件是外事,则停办。否则,将包含所有类别的第2级最低生活标准分类。

关键词是“经典的SVER”

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