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Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
TLDR
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. Expand
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Model selection approaches for non-linear system identification: a review
TLDR
The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. Expand
  • 175
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A Kernel-Based Two-Class Classifier for Imbalanced Data Sets
TLDR
We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. Expand
  • 155
  • 4
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Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach
TLDR
This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. Expand
  • 141
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Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design
TLDR
The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. Expand
  • 95
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Construction of Tunable Radial Basis Function Networks Using Orthogonal Forward Selection
TLDR
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Expand
  • 47
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Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks
  • Xia Hong, C. Harris
  • Mathematics, Computer Science
  • IEEE Trans. Fuzzy Syst.
  • 1 February 2001
TLDR
An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models by incorporating piecewise locally linear model fitting. Expand
  • 26
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Nonlinear Identification Using Orthogonal Forward Regression With Nested Optimal Regularization
TLDR
An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out cross validation. Expand
  • 15
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Online Modeling With Tunable RBF Network
TLDR
In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Expand
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A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems
TLDR
This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. Expand
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