Therefore, it has become important to understand what kind of deep networks are suitable for a given problem.
In this paper we discuss several approaches developed by the authors, providing a roadmap to guide designers to choose the most suitable for a specific problem.
In this special case of metalearning, the aspect of interest is the relationship between data characteristics 1 and algorithm performance, with the final goal of predicting an algorithm or a set of algorithms suitable for a specific problem under study.
These stationary increments may not be suitable for all problems at hand.
A comparison between the empirical performance of the selected sampling methods applied to three numerical examples, including high-order PCE's, high-dimensional problems, and low oversampling ratios, is presented to provide a road map for practitioners seeking the most suitable sampling technique for a problem at hand.
A particular optimization scheme may be uniquely suitable for a specific biomedical problem.
This makes them suitable for a variety of problems, and in the following, we show how they can be employed to lead to an effective colour quantisation algorithm.
Several studies applied image processing techniques to the signal TFD in order to select a methodology suitable for the problem considered, leading to an improvement in terms of precision, resolution, performance or robustness.
Meta-heuristics often make few assumptions about the optimization problem being solved, which makes them suitable for a wide variety of problems.
Our system proceeds in two key phases: analysis, in which we attempt to find a suitable solver for a supplied problem, and canonicalization, in which we rewrite the problem in the selected solver's standard form.
