Results 21 to 30 of about 806,396 (338)
Mathematics, 2022 For a fixed time, t, and a horizon time, b, the probability of default (PD) measures the probability that an obligor, that has paid his/her credit until time t, runs into arrears not later that time t+b.
Rebeca Peláez +2 more
doaj +1 more source
Kernel Methods in Medical Imaging [PDF]
, 2015 We introduce machine learning techniques, more specifically kernel methods, and show how they can be used for medical imaging. After a tutorial presentation of machine learning concepts and tools, including Support Vector Machine (SVM), kernel ridge regression and kernel PCA, we present an application of these tools to the prediction of Computed ...
Charpiat, Guillaume +2 more
openaire +5 more sources
Functional Ergodic Time Series Analysis Using Expectile Regression
Mathematics, 2022 In this article, we study the problem of the recursive estimator of the expectile regression of a scalar variable Y given a random variable X that belongs in functional space.
Fatimah Alshahrani +5 more
doaj +1 more source
Random Forests and Kernel Methods [PDF]
IEEE Transactions on Information Theory, 2016 Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods.
openaire +5 more sources
Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
Applied Sciences, 2023 Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated ...
Qing Zou +3 more
doaj +1 more source
On kernel methods for covariates that are rankings [PDF]
Electronic Journal of Statistics, 2018 Permutation-valued features arise in a variety of applications, either in a direct way when preferences are elicited over a collection of items, or an indirect way in which numerical ratings are converted to a ranking. To date, there has been relatively limited study of regression, classification, and testing problems based on permutation-valued ...
Mania, Horia +4 more
openaire +3 more sources
Instrumental variable regression via kernel maximum moment loss
Journal of Causal Inference, 2023 We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction known as a maximum moment restriction (MMR).
Zhang Rui +3 more
doaj +1 more source
A More Efficient and Practical Modified Nyström Method
Mathematics, 2023 In this paper, we propose an efficient Nyström method with theoretical and empirical guarantees. In parallel computing environments and for sparse input kernel matrices, our algorithm can have computation efficiency comparable to the conventional Nyström
Wei Zhang +3 more
doaj +1 more source
Kernel method for nonlinear Granger causality [PDF]
, 2008 Important information on the structure of complex systems, consisting of more than one component, can be obtained by measuring to which extent the individual components exchange information among each other.
A. Papoulis +7 more
core +3 more sources
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision and machine learning algorithms yield inferior results on ...
Sadeep Jayasumana +4 more
openaire +3 more sources