一节课与以往类似课程学习的比较研究
One vs Previous and Similar Classes Learning -- A Comparative Study
摘要：
在处理多类分类问题时，通常的做法是使用学习范式来构建由一系列二进制分类器组成的模型，该学习范式规定如何构建分类器并将其组合以区分各个类。当新数据进入系统并且模型需要更新时，这些模型通常需要从头开始重新训练。这项工作提出了三种学习范式，允许训练的模型进行更新，而不需要从头开始重新训练。进行了比较分析，以根据基线对其进行评价。结果表明，所提出的范例在更新时比基线更快，其中两个在从头开始训练时也更快，特别是在较大的数据集上，同时保持了相当的分类性能。
英文摘要：
When dealing with multi-class classification problems, it is common practice to build a model consisting of a series of binary classifiers using a learning paradigm which dictates how the classifiers are built and combined to discriminate between the individual classes. As new data enters the system and the model needs updating, these models would often need to be retrained from scratch. This work proposes three learning paradigms which allow trained models to be updated without the need of retraining from scratch. A comparative analysis is performed to evaluate them against a baseline. Results show that the proposed paradigms are faster than the baseline at updating, with two of them being faster at training from scratch as well, especially on larger datasets, while retaining a comparable classification performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
