Sentence examples for multiple classifiers using from inspiring English sources
In future works, we plan to research the methods to increase the matching accuracy and combine multiple classifiers using a training-based method.
Combining multiple classifiers using F3 achieved the best results with a f-measure score of 86.3% for P, 67% for I and 56.6% for O.
This method iteratively chooses random subsets (RS) from U and trains multiple classifiers using bias SVM to discriminate P from each subset RS.
Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an f-measure score of 86.3% for P, 67% for I and 56.6% for O.
Smith et al. [ 4] showed that most of the top NER systems in the BioCreAtIvE II challenge for gene mention tagging combined results from multiple classifiers using simple heuristic rules.
Stephen Bay claims that ensemble of multiple classifiers is an effective technique for improving classification accuracy [2] and propose a combining algorithm for nearest neighbor classifiers using multiple feature subsets.
We utilized a cascaded approach to train multiple support vector machine (SVM) classifiers using combinations of feature subtypes to enable the possibility of maximizing the performance by leveraging different feature sets extracted from multiple levels.
For face score calculations, multiple feature extractors and multiple classifiers are used, while for speech scores, multiple feature extractors, and only single classifiers are used.
From the theoretical results obtained under the assumption of unbiased and uncorrelated estimation errors, simple guidelines for the design of multiple classifier systems using linear combiners are given.
The function 'deleteRepeat' was used to extract multiple classifiers from the data using the HGmultc method for fitting a multiclass logistic regression models to our data.
When the distribution of the data is complex and/or the training set is small compared to the feature dimension, the combined decision of an ensemble of multiple classifiers can be used to improve the performance of a single classification rule [ 13].
