Abstract In recent years, renewable energies have been covering an increasing part of the worldwide electrical power demand. The additional volatility introduced to power grids by weather dependent renewable energy sources, i.e., wind and solar, makes it necessary to improve the accuracy of energy forecasts, so that the underlying electrical grid can be operated in a cost efficient way. Governm...

This article deals with the prediction problem in linear regression where the measurements are obtained using k di¤erent devices or collected from k di¤erent independent sources. For the case of k 1⁄4 2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷc and ŷp are pr...

This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear k...

Semi-supervised learning (SSL) concerns how to improve performance via the usage of unlabeled data. Recent studies indicate that the usage of unlabeled data might even deteriorate performance. Although some proposals have been developed to alleviate such a fundamental challenge for semisupervised classification, the efforts on semi-supervised regression (SSR) remain to be limited. In this work ...

results the command is addpred for logistic regression models. conclusions the stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. materials and methods we have written a stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement ind...

One of the key challenges of climate science today is the understanding and prediction of rainfall and other precipitation events [6]. It holds high values for both scientists and policymakers in terms of obtaining novel scientific insight into the processes of precipitation formation, as well as predictions and associated uncertainties which decide future policy decisions based on them. One of...

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [F...

This paper performs an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the d...

Traditional methods of assessing chemical toxicity of various compounds require tests on animals, which raises ethical concerns and is expensive. Current legislation may lead to a further increase of demand for laboratory animals in the next years. As a result, automatically generated predictions using Quantitative Structure–Activity Relationship (QSAR) modelling approaches appear as an attract...