Kernel-based Modelsdoi:10.1007/978-3-031-01552-6_2Campbell, ColinUniversity of BristolYing, YimingUniversity of Exeter
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly optimal policies, but they are generally restricted to finite domains. Mean-while, function approximation addresses con-tinuous state spaces but typically weak-ens convergence guarantees. In this work, we ...
The kernel function—a function returning the inner product between mapped data points in a higher dimensional space—is a foundational building block for kernel-based learning methods. Such learning takes place in the feature space so long as the learning algorithm can be entirely rewritten so that...
Several previous studies have developed models for predicting the daily truck traffic at seaport terminals using terminal operation data. In this study, two kernel-based supervised machine learning methods are introduced for the same purpose: Gaussian processes (GPs) andε-support vector machines (ε-...
The goal of the experiment is to assess the predictive capability of models built using kernel-based estimators. We consider Multiple Input-Single Output (MISO) models. The temperature from the first node is the output (y_i) and the other 7 represent the inputs (u^j_i, j=1,..,7). ...
Informative input design for Kernel-Based system identification 2016, 2016 IEEE 55th Conference on Decision and Control, CDC 2016 On system identification for ARMAX models based on the variational Bayesian method 2016, 2016 IEEE 55th Conference on Decision and Control, CDC 2016 View all citing artic...
In our experiments, DfM models for 2 process lines are generated based on test patterns, and the results show that the simulated shapes have an area error less than 2percent compared to the real shapes of test patterns and an area error less than 3percent compared to the shapes in typical...
The theoretical derivations also show that kernel-based and spline-based GCV give very similar asymptotic results. This provides us with a solid base to use kernel estimation for mixed-effect models. Simulation studies are undertaken to investigate the empirical performance of the GCV. A real data...
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensivel
We propose a novel kernel-based trend pattern tracking (KTPT) system for portfolio optimization. It includes a three-state price prediction scheme, which extracts both of the following and reverting patterns from the asset price trend to make future price predictions. Moreover, KTPT is equipped ...