提出一种新型核方法，提升动态系统学习的效率与性能。
ROCK: A variational formulation for occupation kernel methods in Reproducing Kernel Hilbert Spaces
- 基于变分框架构建通用核方法理论基础
- 新方法在多数基准测试中更快更准
- 适合研究动态系统建模与核方法的学者
我们为一大类弱形式问题提供了表示定理结果。该理论可应用于传统机器学习、数值方法以及新兴技术。我们将此框架用于推广多变量占用核（MOCK）方法，提出更通用的瑞斯占用核（ROCK）方法，用于从数据中学习动力系统。所提方法在大多数测试基准上均表现出更高的计算效率和更强的性能。
原文摘要 · Abstract (English)
We present a Representer Theorem result for a large class of weak formulation problems. We provide examples of applications of our formulation both in traditional machine learning and numerical methods as well as in new and emerging techniques. Finally we apply our formulation to generalize the multivariate occupation kernel (MOCK) method for learning dynamical systems from data proposing the more general Riesz Occupation Kernel (ROCK) method. Our generalized methods are both more computationally efficient and performant on most of the benchmarks we test against.
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