Kernel-based Modelsdoi:10.1007/978-3-031-01552-6_2Campbell, ColinUniversity of BristolYing, YimingUniversity of Exeter
Kernel-based models for reinforcement learning 来自 ResearchGate 喜欢 0 阅读量: 89 作者:NK Jong,P Stone 摘要: 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...
The mainstream approach to identication of linear discrete-time models is givenby parametric Prediction Error Methods (PEM). As a rule, the model complexity is unknownand model order selection (MOS) is a key ingredient of the estimation process. A dierentapproach to linear system identication ...
A global hidden Markov model may then be constructed by taking the average of the models in the training set, allowing calculation of the extent to which a new data item would stretch the parameters of the existing model—its Fisher score. Such an evaluation requires calculation and comparison ...
The strong performance of the GP andε-SVM models and their relative ease of use make them viable alternative approaches to the MLFNN in port-generated truck traffic predictions. 展开 关键词: ports freight drayage support vector machines gaussian processes neural networks DOI: 10.1061/(ASCE)TE....
·) except that it is assumed to lie in a certain functional space. Hence, kernel-based models are more flexible and more robust to issues like model misspecification. Model (1) looks at the problem of finding differentially expressed metabolite-sets from a different point of view. Iff(·)=...
This kernel describes functions known to be somewhat regular and, under a Bayesian perspective, it models f as a stationary Gaussian process. These features are illustrated in the left panel of Fig. 3, which shows the constant variance of f, and of Fig. 4, which shows normal and independent...
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...
As a consequence, it can be useful to get a more unified view of the problem and to build more powerful predicting models. • Scalability on number of training examples: In typical gene prioritization problems, the number of known disease genes is much smaller than the number of candidates....
The book focuses on a broad range of machine learning algorithms and it is aimed at senior undergraduate students, graduate students and practicing researchers and scientists who want to use and develop the kernels based models rather than simply study them. ...