Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a ...

Reproducing kernel Hilbert space method is utilized in this paper as an efficient approach to solve singular fourth order ...

Kernel density estimation (KDE) is a versatile nonparametric approach to infer continuous probability distributions from finite samples. By superimposing smooth kernel functions—most commonly Gaussian ...

Quantum information scientists have introduced a new method for machine-learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...