论文标题
一声学习的meta-meta分类
Meta-Meta Classification for One-Shot Learning
论文作者
论文摘要
我们提出了一种新方法，称为Meta-Meta分类，以在小型数据设置中学习。在这种方法中，人们使用大量的学习问题来设计学习者的团体，在这些学习者中，每个学习者都有很高的偏见和较低的差异，并且擅长解决特定类型的学习问题。 Meta-Meta分类器学习如何检查给定的学习问题并结合各种学习者以解决问题。 Meta-Meta学习方法特别适合解决几乎没有的学习任务，因为用很少的数据对新的学习问题进行分类要比将学习算法应用于小型数据集更容易。我们通过单次，单级对比的分类任务评估该方法，并表明它能够超越传统的元学习和结合方法。
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.
