问题描述
我正在尝试解决一些分类问题.似乎许多经典方法都遵循类似的范例.也就是说，使用某种训练集训练模型，然后使用它来预测新实例的类标签.
I am trying to solve some classification problem. It seems many classical approaches follow a similar paradigm. That is, train a model with some training set and than use it to predict the class labels for new instances.
我想知道是否可以在范式中引入一些反馈机制.在控制理论中，引入反馈回路是提高系统性能的有效方法.
I am wondering if it is possible to introduce some feedback mechanism into the paradigm. In control theory, introducing a feedback loop is an effective way to improve system performance.
目前，我认为最直接的方法是，首先我们从一组初始实例开始，并使用它们来训练模型.然后，每次模型做出错误的预测时，我们都会将错误的实例添加到训练集中.这不同于盲目地扩大训练集，因为它更具针对性.这可以看作是控制理论语言中的一种负反馈.
Currently a straight forward approach on my mind is, first we start with a initial set of instances and train a model with them. Then each time the model makes a wrong prediction, we add the wrong instance into the training set. This is different from blindly enlarge the training set because it is more targeting. This can be seen as some kind of negative feedback in the language of control theory.
反馈方法是否正在进行任何研究?谁能给我一些启示?
Is there any research going on with the feedback approach? Could anyone shed some light?
推荐答案
想到的领域有两个.
第一个是强化学习.这是一个在线学习范例，可让您在观察结果时获得反馈并更新策略(在本例中为分类器).
The first is Reinforcement Learning. This is an online learning paradigm that allows you to get feedback and update your policy (in this instance, your classifier) as you observe the results.
第二个是主动学习，其中分类器从未分类的示例池中选择示例贴上标签.关键是要让分类器选择标签示例，从而在当前分类器假设下选择困难的示例，从而最大程度地提高标签的准确性.
The second is active learning, where the classifier gets to select examples from a pool of unclassified examples to get labelled. The key is to have the classifier choose the examples for labelling which best improve its accuracy by choosing difficult examples under the current classifier hypothesis.
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