Multi-modal fetal ECG extraction using multi-kernel Gaussian processes This study deals with fetal ECG extraction by multi-modal non-parametric modeling. In a recently proposed non-parametric approach, the fetal and maternal E... B Surisetti,RM Dansereau - IEEE Global Conference on Signal & ...
Ramos, "Multi-kernel Gaussian Processes," in International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011, pp. 1408-1413.A. Melkumyan and F. Ramos, "Multi-Kernel Gaussian Processes", in proc. of the International Joint Conference on Artificial Intelligence (IJCAI),...
The Fredholm kernel in the representation can be constructe... T Sottinen,L Viitasaari 被引量: 0发表: 2019年 Results on Local Times of a Class of Multiparameter Gaussian Processes In this paper,we introduce a class of Gaussian processes Y={Y(t):t∈R_+~N},the so called bi-fractional...
利用相邻 state-action 的空间相关性来加速学习,用 gaussian processes 建模 env dynamics(model-based)/ Q function(model-free),从而得到 model-based model-free 两种 MFRL 算法。算法结构跟 14 年的 MFRL 基本一致。全文没有数学证明。1 intro利用相邻 state-action 的空间相关性来加速学习:通过 Gaussian ...
A kernel-based framework to tensorial data analysis ► Multilinear algebra and kernels: feature space of infinite dimensional tensors. ► Our formalism give rise to product kernels, Gaussian-RBF as a spe... M Signoretto,LD Lathauwer,JAK Suykens - 《Neural Networks》 被引量: 100发表: 2011...
From a probabilistic theory perspective, they are the key in the context of Gaussian processes, where the kernel function is known as the covariance function. The theory of kernel methods for single-valued functions is well established ... Mauricio A. lvarez,L Rosasco,Neil D. Lawrence - Now...
[24] Yu, K. et. al. 2005. Learning Gaussian Processes from Multiple Tasks, ICML2005. [25] Bakker, B. et. al. 2003. Task Clustering and Gating for Bayesian Multi-Task Learning. JMLR2003. [26] Xue, Y. et. al. 2007. Multi-Task Learning for Classification with Dirichlet Process Priors...
Hyper parameters of the kernel function can either be sampled or learned by maximising the log marginal likelihood of the training data37. This setup can be extended to the multifidelity case by creating a representation for the fidelities and concatenating it with the representation of the materia...
Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis A hierarchical statistical model named multiclass kernel-imbedded Gaussian process (mKIGP) is developed under a Bayesian framework for a multiclass ... Z Xin,WK Cheung - 《IEEE/ACM Transactions on Computational Biology & Bioinf...
Important to the performance of the GPR is the choice of kernel. Here we use a radial basis function (RBF) kernel which has three hyperparameters; length, kernel variance, and the standard deviation of the Gaussian noise. These hyperparameters are automatically tuned via GPy. 2.3. Generative ...