(2017)created a reconstruction-based unsupervised feature selection model. 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 ...
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...
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 ...
(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 Classification, Data Analysis, and ...
The algorithm selection task, example input: Definition 1 Avalidation instance(or short, an instance) (P, ) consists of a program P (in our experiments, we consider C programs) and a property . The latter is also called specification and is typically either given externally or written as an...
Note thattensorflow changes fromusing cudnnGetConvolutionForwardAlgorithm to cudnnGetConvolutionForwardAlgorithm_v7 in cudnn8 because the old API was deleted. So the regression perhaps is between these two APIs, but not between v7 and v8. But I have not verified this. ...
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 ...
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 the algorithm to look for those signatures in the data. In addition, ten random clusters were initialized to detect de novo signatures ...
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 ...
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, ...