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Sparse kernel modelling: a unified approach
No full textA unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach
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Orthogonal forward selection for constructing the radial basis function network with tunable nodes
No full textAn orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process
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Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design
No full textA construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values
Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
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Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality
Full text linkAn efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach
C.J. Koch (1932 - )
No full textBiographical, bibliographical, and literary historiography of Australian author C.J. Koch
Demeter Inputs for Chen et al. 2020
No full textThese are the inputs used in Demeter runs for the Chen et al. 2020 submission "Global land use projections for 2015-2100 at 0.05-degree resolution under diverse Shared Socioeconomic Pathways and Representative Concentration Pathways."
The input structure is described in detail in:
Vernon, C.R., Le Page, Y., Chen, M., Huang, M., Calvin, K.V., Kraucunas, I.P. and Braun, C.J., 2018. Demeter – A Land Use and Land Cover Change Disaggregation Model. Journal of Open Research Software, 6(1), p.15. DOI: http://doi.org/10.5334/jors.208
Configuration files were created for each run and are contained in the "config_files" directory
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
Full text linkUsing the correlation criterion to position and shape RBF units for incremental modelling
No full textA novel technique is proposed for the incremental construction of sparse radial basis function (RBF) networks. The correlation between a RBF regressor and the training data is used as the criterion to position and shape the RBF node, and it is shown that this is equivalent to incrementally minimise the modelling mean square error. A guided random search optimisation method, called the repeated weighted boosting search, is adopted to append RBF nodes one by one in an incremental regression modelling procedure. The experimental results obtained using the proposed method demonstrate that it provides a viable alternative to the existing state-of-the-art modelling techniques for constructing parsimonious RBF models that generalise well
Functions of the C-terminal region of chitinase ChiCW from Bacillus cereus 28-9 in substrate-binding and hydrolysis of chitin
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