Sentence examples for in classifying from from inspiring English sources

To meet real-time requirements, the coarse-to-fine strategy was used in classifying from the simple pixel level to the complex region level.

Thus, SWC suffers from poor performance in classifying edges from small complexes, demonstrating the importance of the size-specific modeling of SSS.

The results show that the PDF-based feature selection approach is a reliable technique that is highly competitive with respect to the state-of-the-art techniques in classifying AD from high-dimensional sMRI samples.

Another possible scenario is the comparison of the agreement of two methods in classifying individuals from a given population.

Individual markers are, in general, not very powerful in classifying disease from healthy controls.

Those candidate modules that perform well in classifying samples from multiple conditions are then defined as "core modules".

CD59 and AAT show good sensitivity and selectivity (AUC = 0.888 and 0.819, respectively) in classifying normoalbuminuric from high albuminuric (de novo or maintained).

Indeed, the integrin expression is able to define the cell phenotype and seems to be useful in classifying MSCs from various tissues besides the well-known MSC markers we have reported before [ 13].

In terms of diagnosing cancer from normal specifically, two groups from Johns Hopkins [ 2, 3] have used different methods to analyse the data being collected by ONCOMINE http://www.oncomine.com and have attempted to establish a multi-tissue cancer signature and have claimed and demonstrated success in classifying cancer from normal tissue.

For some traits, particularly ones that rely upon qualitative diagnosis, care is taken to ensure that the same diagnostic criteria are used [ 5], but there is always the possibility that different individuals will interpret the criteria in their own way, leading to heterogeneity in classifying individuals from one sample to the next.

We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature.