However, using instead an optimisation algorithm to find the optimal number of clusters would make semi-automatic exploration of input-output relationships of computational models possible.
We build performance models for multiple machines using support vector machines (SVMs) and show that only a sparse exploration of the input space is sufficient to accurately predict the best choice of algorithm over a wide range of possible inputs.
At the end of the learning process, the view allows the interactive exploration of the input data and the extraction of clusters.
In addition to the exploration of different input features, we examined the use of linear regression on the GC content with each of these feature sets in advance of providing them to the one-class SVM.
By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models.
We present a parsimonious agricultural land-use model that is designed to replicate global land-use change while allowing the exploration of uncertainties in input parameters.
This study uses previous research as a point of departure, and builds on this through a number of improvements including: (1) time-based modelling (2) more extensive exploration of uncertainty around input parameters and assumptions, (3) more conservative assumptions around changes in PA over time that underestimated benefits of PA and (4) use of meta-analysed effectiveness data.
This study builds on the current limited economic literature through a number of improvements including: (1) time-based modelling, (2) more extensive exploration of uncertainty around input parameters and assumptions, (3) more conservative assumptions around changes in PA over time and (4) use of meta-analysed effectiveness data.
ANN is a non-linear mathematical model that is inspired by the structural and functional aspects of neuron in exploration of a group of input data (through training and testing) as function of output data (including relative error) and to envisage the performance of the given system (Cavas et al. 2011).
This efficiency enables in-depth analysis of the influences of different factors, as well as explicit exploration of all possible regulatory input combinations.
