论文标题
课程学习中评分功能的发展和比较
Development and Comparison of Scoring Functions in Curriculum Learning
论文作者
论文摘要
课程学习是按照有意义的顺序而不是随机顺序向机器学习模型的样本呈现。课程学习的主要挑战是确定如何对这些样本进行排名。样本的排名由评分函数表示。在这项研究中，使用数据集功能，使用要训练的模型以及使用另一个模型及其合奏版本比较评分功能。对4张图像和4个文本数据集进行了实验。对于文本数据集的评分函数之间没有发现显着差异，但是与经典模型培训相比，使用转移学习创建的评分功能以及图像数据集的其他评分功能获得了显着改进。它表明，正在等待针对文本分类任务找到不同的新评分功能。
Curriculum Learning is the presentation of samples to the machine learning model in a meaningful order instead of a random order. The main challenge of Curriculum Learning is determining how to rank these samples. The ranking of the samples is expressed by the scoring function. In this study, scoring functions were compared using data set features, using the model to be trained, and using another model and their ensemble versions. Experiments were performed for 4 images and 4 text datasets. No significant differences were found between scoring functions for text datasets, but significant improvements were obtained in scoring functions created using transfer learning compared to classical model training and other scoring functions for image datasets. It shows that different new scoring functions are waiting to be found for text classification tasks.
