论文标题
概括误解机器
Generalization-Memorization Machines
论文作者
论文摘要
正确对培训数据进行分类而不合适的是机器学习的目标之一。在本文中，我们提出了一种概括误解机制，包括概括误解决策和记忆建模原理。在这种机制下，基于错误的学习机器提高了他们对训练数据的记忆能力而无需过度拟合。具体而言，通过应用此机制提出了概括性迁移计算机（GMM）。 GMM中的优化问题是二次编程问题，可以有效地解决。应该注意的是，最近提出的概括误解内核和相应的支持向量机是我们GMM的特殊情况。实验结果表明，所提出的GMM在记忆和概括方面的有效性。
Classifying the training data correctly without over-fitting is one of the goals in machine learning. In this paper, we propose a generalization-memorization mechanism, including a generalization-memorization decision and a memory modeling principle. Under this mechanism, error-based learning machines improve their memorization abilities of training data without over-fitting. Specifically, the generalization-memorization machines (GMM) are proposed by applying this mechanism. The optimization problems in GMM are quadratic programming problems and could be solved efficiently. It should be noted that the recently proposed generalization-memorization kernel and the corresponding support vector machines are the special cases of our GMM. Experimental results show the effectiveness of the proposed GMM both on memorization and generalization.
