Sentence examples for of multiple classifications from inspiring English sources

Thus, strategies for using TRIBE-MCL range from simply using program defaults [22] or arbitrary user-defined values [23] to the generation of multiple classifications using different parameter values [24] and the use of parameter values found to be globally optimal with respect to some empirical quality measure [25].

An alternative here may be the use of Multiple Classification Analysis (MCA), also known as factorial ANOVA.

We describe several ways of measuring the categorization success that account for the attribution of multiple classification codes to each patent document.

In this context, the present study introduces WSF2, a novel web spam filtering framework specifically designed to take advantage of multiple classification schemes and algorithms.

We present a hybrid representation of architectural precedents that separates precedent instances from the concepts they embody, where the concepts are defined in terms of multiple classification taxonomies.

In this paper, we propose an environment recognition system for a wheeled mobile robot that consists of multiple classification analyses to make the robot more adaptive to various environments by selecting a suitable system such as decision making, navigation and controller using the result of the environment recognition system.

The heterogeneity of signs and symptoms has led to the development of multiple classification criteria.

We propose a novel and general framework called the multithreading cascade of Speeded Up Robust Features (McSURF), which is capable of processing multiple classifications simultaneously and accurately.

Annual spatially-explicit maps were developed from 2005 to 2011, using monthly composites of MODIS, multiple classifications (ensemble classifier) with decision trees and attributes from auxiliary datasets to characterize 15 land-cover classes in Mexico [53].

Annual spatially-explicit maps were developed from 2005 to 2011, using monthly composites of MODIS, multiple classifications (ensemble classifier) with decision trees and attributes from auxiliary datasets to characterize 15 land-cover classes in Mexico [ 53].

Event detection by stochastic mapping is determined by a varying cutoff value (posterior probability of event), thus multiple classifications are possible with various levels of sensitivity (=true positive rate [TPR]) and specificity (=1 − false positive rate [FPR]).