Meta-Meta Classification for One-Shot Learning

Meta-Meta Classification for One-Shot Learning

We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem.The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. The meta-meta learning approach is especially suited to solving few-shot learning tasks, as it is easier to learn to classify a new learning problem with little data than it is to apply a learning algorithm to a small data set. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.

一键式学习的元元分类

我们提出了一种称为元元分类的新方法，用于在小数据环境中进行学习。在这种方法中，人们使用大量的学习问题来设计一组学习者，其中每个学习者具有较高的偏见和低方差，并且善于解决特定类型的学习问题。.. 元元分类器学习如何检查给定的学习问题并结合各种学习者来解决问题。元元学习方法特别适合解决一次性学习任务，因为与使用学习算法应用于较小的数据集相比，它更容易学习用很少的数据对新的学习问题进行分类。我们在一次完成的一类，一类对所有分类任务上评估了该方法，并表明该方法能够胜过传统的元学习和组合方法。 （阅读更多）