Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3 ) scaling in computational time to O(nm2 ). Thus far, wo...
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Abstract. In multispectral imaging systems, the accuracy of reflectance estimation can be degraded by the nonlinearity in imaging process, which is due to non-Gaussian distribution...
Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among ...
Novi Quadrianto, Kristian Kersting, Mark D. Reid, ...
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. ...
Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin...