Sentence examples for diversity method from inspiring English sources
In this paper, we proposed a slot allocation scheme for multicell OFDMA mobile WiMAX system using zone switching diversity method.
Diversity indices: In the present study, the diversity indices of phytoplankton species have been calculated in terms of Species diversity method given by Shannon (2001), Species richness by Menhinick (1964) and Evenness by Pielou (1975) as follows: Species diversity method given by Shannon (2001), Species richness by Menhinick (1964) and.
Fitness calculation focuses on pharmacophoric similarity with the known ligands while parent selection uses a graph-based diversity method in order to preserve structural diversity of the evolved molecules.
The "Haplotype Diversity" method is used for partitioning the data into haplotype blocks [37] (more details provided in Materials and Methods S1).
For the typing methods and data sets explored here, independence is an exception that happens mainly when we probe the relationship between the classifications of a low diversity method with the classifications of others with very high diversities.
Among the many methods available, we chose the limited haplotype diversity method by Patil et al. [ 13] and extended by Zhang et al. [ 22], as implemented in the HapBlock software [ 23] (HapBlock), and the three LD-based methods implemented in the Haploview software [ 24]: the Gabriel et al. approach [ 15] (DprimeCI), the Solid Spine of LD (SSD), and the four gamete test [ 25] (4Gamete).
A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations.
Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS) combines molecular evolution and maximum diversity methods to create libraries representative of various chemical spaces [53].
Hence, the best combination of all microphone signals can be chosen for each bin in the frequency domain, e.g., by applying diversity methods as in[3, 4].
The main conclusion of our work is that binarizing multi-class problems is the key to obtain benefit from those diversity methods.
In this contribution, we study how to apply some conventional diversity methods –bagging and label switching– to a general deep machine, the stacked denoising auto-encoding classifier, in order to solve a number of appropriately selected image recognition problems.
