使用LIBSVM来预测用户的真实性 [英] Using LIBSVM to predict authenticity of the user

问题描述

我计划使用LibSVM来预测Web应用程序中的用户真实性。
（1）收集有关特定用户行为的数据（例如，登录时间，IP地址，国家等）
（2）使用收集的数据训练SVM
（3）使用实时要比较的数据并在真实性水平上生成输出

I am planning on using LibSVM to predict user authenticity in web applications. (1) Collect Data on particular user behavior(eg. LogIn time, IP Address, Country etc.) (2) Use Collected Data to train an SVM (3) Use real time data to compare and generate an output on level of authenticity

有人可以告诉我如何使用LibSVM做这样的事情？ Weka能否对这些类型的问题有所帮助？

Can some one tell me how can I do such a thing with LibSVM? Can Weka be helpful in these types of problems?

推荐答案

您提到的三个步骤是解决方案的概述。更详细一些：

The three steps you mention are an outline of the solution. In some more detail:

1. 确保获得大量标记的数据，即用真实/标注的行为日志非正品。 （如果没有标记数据，您将进入半监督学习的高级领域，或者必须考虑其他解决方案。）


2. 根据数据设计一些功能你认为很好地预测真实性。尝试使用该方法并对其进行优化，直到它通过某种统计标准运行良好。使用十倍交叉验证确保您的身份


3. LibSVM可以输出概率估计值及其答案;请参阅其手册的第8部分。

1. Make sure you get plenty of labeled data, i.e. behavior logs annotated with authentic/non-authentic. (Without labeled data, you get into the pretty advanced field of semisupervised learning, or must consider other solutions.)
2. Design a number of features based on the data that you think predict authenticity well. Try the method and refine it until it works well enough by some statistical standard. Use ten-fold cross validation to assure you're not overfitting.
3. LibSVM can output a probability estimate along with its answer; see section 8 of its manual.

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