TL;DR提出用于动态环境下智能修正和自我更新的数据处理系统的框架,称为Self-Updating Models with Error Remediation(SUMER),通过半监督学习和噪声处理技术,自我更新模型以适应新数据,并使用error remediation调整数据效果。该框架在各种数据集和迭代中具有优异的表现。
Abstract
Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models