Sentence examples for other identification methods from inspiring English sources

We also discuss how dominance analysis can be combined with other identification methods to recover a larger part of the architecture, and a novel approach to merge the results from different identification methods into a single data structure, a semi-lattice over the power set of all system classes, that permits a unified view of the different results.

Although this is largely ignored by other identification methods, system order estimation should be an integral part of a system identification algorithm.

Both criteria can be used to solve model order selection problem for autoregressive-moving-average (ARMA) model or other identification methods successfully [13].

The detection of two toxins in a preparation can be difficult for some other identification methods.

Most studies confirmed the need for other identification methods along with the API based system to avoid errors and misidentifying isolates [ 24].

PCR-based microsatellite polymorphisms have proved their power in genetic linkage analysis and other identification methods, due to their high information content and even distribution over the chromosomes.

Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data.

John Daugman, a British computer scientist who developed the technique to codify the iris, said he thought this and other biometric identification methods would become more widespread because they were more reliable than traditional methods.

As an example, identification of the heat convection constants of a typical microelectronic package are performed using the present method and the performance is examined comparing with other inverse identification methods.

The percentage of the data set where a SoM is identified in the top two (top-2%) and top three (top-3%) predictions was calculated to allow comparisons to other SoM identification methods.

This integrative approach allows to systematically explore functional modules in the network and it is highly complementary to existing bi-clustering and other module identification methods.