Sentence examples for recognition prediction from inspiring English sources
The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling.
The motivation for the creation of the grey fuzzy Markov pattern recognition prediction (GFMAPR) model arose from the observation on preliminary analysis that randomly summative and multiplicative relationships existed between industrial accidents data at different points within an existing data set.
Several types of ANN have been extensively used for various purposes, such as classification, pattern recognition, prediction and forecasting, process control, optimization and decision support [ 21].
Secondary structure prediction was performed with PsiPred and ProfSec via the meta server, collected models were screened with 3D-Jury [ 83], a consensus fold recognition prediction method, for final predictions.
In visual word recognition, predictions were shown to pre-activate form-specific patterns of expected words (e.g., [35]).
This is the performance of TEES without the influence of the named-entity recognition predictions of our text mining pipeline, as only gold-standard documents are used during the training step.
Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system.
Kernel trick improves largely the performance of learning system including recognition, clustering, prediction through the nonlinear kernel mapping from the input data space to output data space.
In this model, the usage of networks with only forward calculations is needed in pattern recognition and prediction, and the calculation can be executed very quickly.
Our methodology has been evaluated using well-known datasets and performance metrics specifically designed for online and real time action recognition and prediction.
The proposed models were effective in recognition and prediction of different mixture proportions, thereby allowing the distinction of genuine coffee by principal component analysis and linear discriminant analysis.
