Sentence examples for multiple classifiers based from inspiring English sources

Even though there have been many studies on QBH, few have combined multiple classifiers based on various fusion methods.

While still a very active area of research [ 35, 36], the fusion of multiple classifiers based on support vector machines, neural networks, and discriminant analysis has had success, such as the classification of bacteria [ 37], handwriting images [ 38], credit scores [ 39], and remote sensing [ 40].

In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification.

We used the provided CHEMDNER corpus, the DDI corpus and the patents corpus for training multiple CRF classifiers, based on the different types of entities considered on each dataset.

As mentioned above, the main reason is that multiple discriminative classifiers based on hierarchically clustered speaker groups are employed to map the speech utterance into discriminative language characterization score vector space, which not only represents enhanced language information but also compensates for intralanguage and interspeaker variability.

A multiple kNN classifier based decision level fusion classification architecture is proposed for multimodal speech prosody and vocal source expert fusion.

Bearing in mind, complexity of molecular changes in tumors, the widespread skepticism about a single 'cancer marker' as well as possible differences in histological subtypes or other features of PTC, we decided to use SVM, a routine machine-learning approach to construct classifiers based on multiple features of the analyzed objects.

Classifiers based on genes had a very large variance, compared to the BC module-based classifier.

Accordingly we used four types of classifiers including classifiers based on tree, classifiers based on KNN, classifiers based on rule, and classifiers based on function.

To build classifiers based on aCGH profiles.

In order to accomplish this, we have used multiple training sets and the associated test sets to assess each trained classifier, and used a combined classifier based on all the trained individual classifiers (based on specific training set) using a majority-rule vote at the end.