Ensemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where inco...

A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last ∼ 20 000 yr. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation–age data and uplift rates, with an aggregate score computed for each run that measures overall...

Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gløshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best n...

Real-time structural health monitoring requires dynamic sensing of distributed strain and temperature. Brillouin Optical Time Domain Analysis (BOTDA) and Rayleigh Coherent Optical Time Domain Reflectometry (COTDR) are promising candidates to achieve dynamic sensing. A noise model with specific parametric simulation of independent laser and detector noise sources has been developed. Although ens...

Current theories of heteropolymers are inherently macroscopic, but are applied to mesoscopic proteins. To compute the free energy over sequences, one assumes self-averaging--a property established only in the macroscopic limit. By enumerating the states and energies of compact 18, 27, and 36mers on a lattice with an ensemble of random sequences, we test the self-averaging approximation. We find...

In this paper, the ‘ensemble block’ time average procedure and the Ogive function are applied to three ChinaFLUX sites to determine the low-frequency contribution to eddy fluxes. It is found that both methods give similar results. They show that 30 min flux-averaging periods are sufficient to capture all flux scales over a wheat field, but 60 min or longer is necessary over forests. Our results...

Simulation of turbulence boiling, generation of vapour and predication of its behaviour are still subject to debate in the two-phase flow area and they receive a high level of worldwide attention. In this study, a new arrangement of the three dimensional governing equations for turbulence two-phase flow with heat and mass transfer are derived by using ensemble averaging two-fluid model and ...

There are situations in which lexicon-based methods for Sentiment Analysis (SA) are not able to generate a classification output for specific instances of a given dataset. Most often, the reason for this situation is the absence of specific terms in the sentiment lexicon required in the classification effort. In such cases, there were only two possible paths to follow: (1) add terms to the lexi...

We propose a novel method of reconstructing the topology and interaction functions for a general oscillator network. An ensemble of initial phases and the corresponding instantaneous frequencies is constructed by repeating random phase resets of the system dynamics. The desired details of network structure are then revealed by appropriately averaging over the ensemble. The method is applicable ...

Abstract This paper presents an optimization approach—residual-based bootstrap averaging (RBBA)—for different types of forecast ensembles. Unlike traditional residual-mean-square-error-based ensemble approaches, the RBBA method attempts to find optimal weights in and allows for their combination into most effective additive forecast. In method, all forecasts obtain residuals that are statistica...