Sentence examples for the boosting process from inspiring English sources

We argue that the context information about a face can be advantageously used in the deeper nodes of the boosting process when the features derived from the central region of the face do not provide any further benefit.

However, it can be empirically observed that, in the deeper nodes of the boosting process, the non-face examples collected by bootstrapping are in fact very similar to the face examples, and the error rate of those feature-based weak classifiers is very close to 50%.

The boosting process requires some amount of labeled target data.

In general, lower indices correspond to more influential nodes that were added earlier in the boosting process.

The selection of each new weak classifier can be viewed as a feature selection method in the boosting process.

Each phase of the boosting process creates a submodel, which we will refer to as expert for disambiguation purposes.

The Adaboost algorithm, in general, assigns a same weight to each training datum on the first round of a boosting process (Freund and Schapire, Eur. Conf. Comput. Learn. Theory 23 37 , 1995.

The Boost process evaluation protocol (Aarestrup AK, Due P, Suldrup Jørgensen T, Krølner R: A six-step protocol to systematic process evaluation of multicomponent cluster-randomized health promoting interventions illustrated by the Boost study, submitted) was useful in securing the collection of relevant qualitative data for exploring the implementation process.

Semi-structured interview guides for pupils, teachers and suppliers and an observation check list were developed specifically for the Boost study, guided by the Boost process evaluation protocol and inspired by tools applied in the HEIA study [ 33] and Pro Children study [ 34] (see Additional file 1, Additional file 2, Additional file 3, Additional file 4).

The decision trees were sequentially added to the model using a "boosting" process that enhances the accuracy of prediction.

This is of equal importance to better understand, monitor, analyze, and plan smart sustainable cities for the purpose of catalyzing and boosting the process of sustainable development towards achieving the long-term goals of sustainability.