论文标题
将和限制纳入多任务高斯流程
Incorporating Sum Constraints into Multitask Gaussian Processes
论文作者
论文摘要
可以通过调整机器学习模型来尊重现有的背景知识来改进。在本文中，我们考虑了多任务高斯流程，其背景知识的形式为约束，需要特定的输出总和是恒定的。这是通过根据约束履行来调节先前的分布来实现的。该方法允许线性和非线性约束。我们证明，与标准高斯流程相比，构造可以提高总体预测准确性，并且可以提高构造的总体预测准确性。
Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge in the form of constraints that require a specific sum of the outputs to be constant. This is achieved by conditioning the prior distribution on the constraint fulfillment. The approach allows for both linear and nonlinear constraints. We demonstrate that the constraints are fulfilled with high precision and that the construction can improve the overall prediction accuracy as compared to the standard Gaussian process.
