Support vector machines (classification and regression) are powerful machine learning techniques for crisp data. In this paper, the problem is considered for interval data. Two methods to deal with the problem using support vector regression are proposed and two new methods for evaluating performance for estimating prediction interval are presented as well.

In construction planning, decision making has a great impact on final project performance. Hence, it is essential for managers to assess the planning and make informed decisions. However, disproportionately large uncertainties occur during stage; in worst case, reliable probability distributions of are sometimes unavailable due lack information before implementation. This situation constitutes ...

This paper proposes a complete-information-based principal component analysis (CIPCA)-back-propagation neural network (BPNN)_ fault prediction method using real unmanned aerial vehicle (UAV) flight data. Unmanned vehicles are widely used in commercial and industrial fields. With the development of UAV technology, it is imperative to diagnose predict faults improve their safety reliability. The ...

This paper introduces an approach to fitting a constrained linear regression model to interval-valued data. Each example of the learning set is described by a feature vector for which each feature value is an interval. The new approach fits a constrained linear regression model on the midpoints and range of the interval values assumed by the variables in the learning set. The prediction of the ...

OBJECTIVES To examine the value of pre-induction sonographic assessment of cervical length, posterior cervical angle and occipital position in the prediction of the induction-to-delivery interval within 24 h, the likelihood of vaginal delivery within 24 h, the likelihood of Cesarean section and to compare sonographic assessment with the Bishop score. METHODS In 604 singleton pregnancies, indu...

Prediction intervals are needed to quantify prediction uncertainty in, for example, warranty prediction. Naïve prediction intervals (also known as intervals from the “plug-in method”) ignore the uncertainty in parameter estimates. Simulation-based calibration methods can be used to improve prediction interval coverage probabilities. This article evaluates the coverage probabilities for naive an...

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap m...

AIMS Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS Digital electrocardiograms from 173 529 primary care patients aged 50-90 years were collected during 2001-11. The Fram...