The reconstruction function learning process was incorporated into gene selection in this model. In another case, Jiang, Wu, Yu, and Chen (2019) proposed an embedded semisupervised gene selection algorithm that used a Bayesian technique to dynamically select the informative genes while also developing...
4.3FL algorithm selection FLalgorithm selectionis an important consideration in the design of efficient and effective FL systems forvehicular communications. The selection of the appropriate FL algorithm can have a significant impact on the performance and accuracy of the learning model, as well as the...
A novel stochastic search variable selection algorithm in normal linear regression problems, termed as group informed variable selection algorithm (GiVSA), which uses the known group structure efficiently to explore the model space without discarding any
cudnn8.0.2, TF_CUDNN_USE_AUTOTUNE=1: 39.4it/s The results show that: (1) the algorithm selection heuristics are much worse than optimal (2) the algorithm selection heuristics has a 10% regression in cudnn8. Note thattensorflow changes fromusing cudnnGetConvolutionForwardAlgorithm to cudnnGe...
An experimental comparison of a genetic algorithm and a hill-climber for term selection - MacFarlane, Secker, et al. () Citation Context ...might be trapped in local optima, this potential drawback does not seem to hinder our search for efficient ER-fMRI designs. Similar observations can ...
previously identified to be commonly active in lung cancer were included as priors (SBS1, SBS2, SBS4, SBS5, SBS13 and SBS17b). This means for each of them, a cluster was initialized at the start of the algorithm and their trinucleotide pattern was provided as prior knowledge to force th...
Serikova, E., Zhuk, E. (2006). A New Effective Algorithm for Stepwise Principle Components Selection in Discriminant Analysis. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classif...
We also studied the applicability of the new approach in the important field of design optimization. In order to reduce the number of time consuming precise function evaluations, the algorithm will be supported by approximate function evaluations based on Kriging metamodels. First results on an ...
The algorithm calculates an adaptive threshold value from the load information of cells in the graph and detects overloaded cells that exceed this threshold value. Clusters are dynamically created from suitable neighbouring cells selected from an overloaded cell and its neighbours. To balance loads, ...
Selection algorithm Incomputer science, aselection algorithmis analgorithmfor finding thekth smallest number in alistorarray; such a number is called thekthorder statistic. This includes the cases of finding theminimum,maximum, andmedianelements. There are O(n) (worst-case linear time) selection ...