Improved structure selection for nonlinear models based on term clustering
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Improved structure selection for nonlinear models based on term clustering by L. A. Aguirre

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Published by University of Sheffield, Dept. of Automatic Control and Systems Engineering in Sheffield .
Written in English


Book details:

Edition Notes

StatementL.A. Aguirre and S.A. Billings.
SeriesResearch report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.509, Research report (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.509.
ContributionsBillings, S. A.
ID Numbers
Open LibraryOL19558931M

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The consequences of doing this are i) a drastic reduction in the size of the set of candidate terms and consequently a substantial gain in computation time is achieved, ii) the final estimated model is more likely to reproduce the dynamics of the original system and iii) the final model is more robust to : L.A. Aguirre and S.A. Billings. Improved structure selection for nonlinear models based on term clustering. In this paper the concepts of term clusters and cluster coefficients are defined and used in the context of system identification. It is argued that if a certain type of term in a nonlinear model is spurious, the respective cluster coefficient is small compared with the coefficients of the other clusters represented in the model. However, relatively little research has been done into the structure selection for nonlinear models. In the paper of Aguirre and Billings, the concepts of term clusters and cluster coefficients are defined and used in the context of system identification. It is argued that if a certain type of term in a nonlinear model is spurious, the respective cluster coefficient is small compared with the coefficients of the other clusters represented in the by: While advanced FNN uses nonlinear input–output data-based models for the model-based selection of the threshold constant that is used to compute the percentage of false neighbors, the.

tially based procedure, model-based clustering can use model selection methods to make this decision. Bayes factors (Kass and Raftery ) are used to compare the models. This per-mits comparison of the nonnested models that arise in this context. The Bayes factor for a model M 1 against a competing model M 2 is equal to the posterior odds for M 1 against M 2. 2 Model-based clustering Model-based clustering consists of modelling the data to be classi ed by a mixture distribution and of associating a class with each of the mixture components. Embedding cluster analysis in this precise framework is useful in many aspects. In .   study clustering methods applicable to time series with a general and dependent structure. We propose a copula-based distance to measure dissimilarity among time series and con-. sider an estimator for it, where the strong consistency of the estimator is guaranteed. Enhancing the selection of a model-based clustering 3 1 Introduction In model selection, assuming that the data arose from one of the models in com-petition is often somewhat unrealistic and could be misleading. However this assumption is implicitly made when using standard model selection criteria such as AIC or BIC.

Variable Selection Methods for Model-based Clustering Michael Fop ∗and Thomas Brendan Murphy UniversityCollegeDublin e-mail:@;@ Abstract: Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowa-File Size: 1MB. Frequency-based feature selection; Feature selection for multiple classifiers; Clustering in information retrieval; Problem statement. Cardinality - the number of clusters. Evaluation of clustering; K-means. Cluster cardinality in K-means. Model-based clustering; References and further reading; Exercises. Hierarchical clustering. model-based clustering Model-based clustering monotonicity Hierarchical agglomerative clustering multiclass classification Classification with more than multiclass SVM References and further reading multilabel classification Classification with more than multimodal class Rocchio classification Multinomial Naive Bayes, random variable X / U. Abstract: While clustering has been well studied in the past decade, model selection has drawn much less attention due to the difficulty of the problem. In this paper, we address both problems in a joint manner by recovering an ideal affinity tensor from an imperfect input. By taking into account the relationship of the affinities induced by the cluster structures, we are able to significantly Cited by: 2.