Sentence examples for robust ensemble from inspiring English sources

Furthermore, concepts of negative correlation topology design and hierarchical pair competition based parallel training are extracted from literature to form a diverse and robust ensemble identifier.

In this paper, a hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm (HCMRKR) with robust ensemble Pareto dominance is proposed for big data analysis.

Secondly, a robust ensemble strategy of reduction Pareto equilibrium is adopted to help elitist leaders from the Pareto front region to conduct the ensemble game of reduction subsets in different niche conic subspaces.

It looks a lot like this: a robust ensemble of established comedy guys, one hot woman who can deliver a funny line (but is hardly a "comedienne") and then maybe one other role for a previously proven female commodity: Tina, Amy, etc.

That is, the base learners utilized in a robust ensemble classifier should be of high classification accuracy and avoid making coincident misclassification errors which in turn necessitate the diverse learners.

The motivation for this study is to utilize robust ensemble methods that are less sensitive to the selection of genes and are capable of removing the uncertainties of gene expression data.

Two of the key characteristics for building a robust classifier ensemble include (a) the diversity among the classifier models in the ensemble [ 84] and (b) the reasonably high accuracy of the individual members in the ensemble.

But the real centerpiece here was Corelli's Concerto Grosso in D (Op. 6, No. 4), a zesty attention-grabber packed with vivid solo passages — even the continuo players had a share of the spotlight — and a robust full ensemble finale.

In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag.

The experimental validation of proposed robust layered ensemble model included the test of the clustering procedure based on the Graded Possibilistic c-Means on an artificial data set with built-in concept drift and shift.

The proposed robust layered ensemble model (RLEM) for short-term traffic forecasting consists of two layers as shown in Fig. 1 and is able to track the changes in data streams, such as traffic flows, and to use this information to improve the forecasting accuracy.