我正在准备计算机视觉课程的任务,其中包括在从图像中提取特征之后训练一个简单的分类器。 由于机器学习不是这里的主要话题,我不希望学生从头开始实施学习算法。 所以,我必须向他们推荐一些参考实现。 我相信决策树分类器适合于此。
问题是这个类允许的语言种类很多:C ++,C#,Delphi。 此外,我不希望学生花费大量时间来处理任何技术问题,如链接库。 WEKA非常适合Java。 我们也可以将OpenCV与所有包装器一起使用,但它非常大而且笨拙,而我想要简单而甜蜜的东西。
那么,你知道用于学习决策树的任何简单的C ++ / C#/ Delphi库吗?
I am preparing a task for computer vision class, which involves training a simple classifier after extracting features from images. Since machine learning is not the main topic here, I don't want students to implement a learning algirithm from scratch. So, I have to recommend them some reference implementations. I believe the decision tree classifier is suitable for that.
The problem is the variety of languages allowed for the class is quite large: C++, C#, Delphi. Also, I don't want students to spend a lot of time to any technical issues like linking a library. WEKA is great for Java. We also can use OpenCV with all the wrappers, but it is quite big and clumsy while I want something simple and sweet.
So, do you know any simple C++/C#/Delphi libraries for learning decision trees?
最满意答案
我知道这些库,我最近才使用过这些库。 这两个是Waffles和Tilburg-Based Memory Learner (TiMBL)。 两者都是免费的和开源的(分别是lgpl和GNU gpl)。 此外,两者都是稳定,成熟的库。 Waffles创建并且目前由一位开发人员维护,而TiMBL我认为是一个学术项目(针对语言学领域)。
在这两个中,我只使用了Waffles中的决策树模块(在GDecisionTree类中,请参阅此处的文档)Waffles可能是这里的首选库,因为它包含一组适用于描述性统计的功能以及用于诊断的绘图功能,可视化解决方案空间,以及诸如此类的东西。 图书馆作者(Mike Gashler)还包括一组演示应用程序,但我不记得其中一个应用程序是否为决策树。
我已经使用了Waffles Library中的几个类(包括决策树类),我当然可以推荐它。 我无法再谈论基于Tilburg的记忆学习者,因为我从未使用过它的决策树类。
I know of such libraries, only one of which i have used recently. The two are Waffles and the Tilburg-Based Memory Learner (TiMBL). Both are free and open-source (lgpl and GNU gpl, respectively). In addition, both are stable, mature libraries. Waffles was created and is currently maintained by a single developer, while TiMBL i believe is an academic project (directed at the field of Linguistics).
Of these two, i have only used the decision tree module in Waffles (in class GDecisionTree, see the documentation here) Waffles might be the library of choice here because it includes a decent set of functions for descriptive statistics as well as plotting functions for diagnostics, to visualize the solution space, and whatnot. The Library author (Mike Gashler) also included a set of demo apps, though i don't recall if one of these apps is a decision tree.
I have used several of the classes in the Waffles Library (including the decision tree class) and i can certainly recommend it. I'm unable to say anything more about the Tilburg-Based Memory Learner because i have never used its decision tree class though.
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