Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters

Prediction of Liquid-Liquid Equilibria of Binary Systems Containing Alcohols Using EoS-GE Models

Statistical mechanical approaches to models with many poorly known parameters.

Models of biochemical regulation in prokaryotes and eukaryotes, typically consisting of a set of first-order nonlinear ordinary differential equations, have become increasingly popular of late. These systems have large numbers of poorly known parameters, simplified dynamics, and uncertain connectivity: three key features of a class of problems we call sloppy models, which are shared by many oth...

Prediction of Mechanical Lung Parameters Using Gaussian Process Models

Mechanical ventilation can cause severe lung damage by inadequate adjustment of the ventilator. We introduce a Machine Learning approach to predict the pressure-dependent, non-linear lung compliance, a crucial parameter to estimate lung protective ventilation settings. Features were extracted by fitting a generally accepted lumped parameter model to time series data obtained from ARDS (adult re...

Bayesian Subset Selection of Binomial Parameters Using Possibly Misclassified Data

Fast Prediction with SVM Models Containing RBF Kernels

We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and makes no assumptions regarding data normalization. The prediction speed of a...