论文标题
人与监督的机器学习：谁能更快地学习模式？
Human vs. supervised machine learning: Who learns patterns faster?
论文作者
论文摘要
在科学研究和使用SML中，正在讨论监督机器学习（SML）的能力，尤其是与人类能力相比。这项研究提供了一个答案，说明当培训数据有限时，人类和机器之间的学习绩效如何不同。我们设计了一个实验，其中44个人和三种不同的机器学习算法在标记的培训数据中识别模式，并且必须根据他们发现的模式标记实例。结果表明，性能与任务的基本模式之间的依赖性很高。尽管人类在所有模式中的表现相似，但机器在我们的实验中显示出较大的性能差异。在看到了20个实验实例之后，人类的绩效不再改善，这与认知超负荷的理论有关。机器学习较慢，但在4种模式中的2个中，可以达到相同的水平甚至超过人类的表现。但是，与人类相比，机器需要更多的实例。由于结合输入功能的难度，其他两种模式的机器性能较低。
The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features.
