The nature of statistical learning theory
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Support vector density estimation
Advances in kernel methods
Swarm intelligence
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Three learning phases for radial-basis-function networks
Neural Networks
Atomic Decomposition by Basis Pursuit
SIAM Review
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning
An Attempt for Coloring Multichannel MR Imaging Data
IEEE Transactions on Visualization and Computer Graphics
Multivariate Density Estimation: an SVM Approach
Multivariate Density Estimation: an SVM Approach
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Fast learning in networks of locally-tuned processing units
Neural Computation
International Journal of Systems Science
Particle swarm optimization for sorted adapted Gaussian mixture models
IEEE Transactions on Audio, Speech, and Language Processing
Optimization of power allocation for interference cancellation with particle swarm optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal forward selection for constructing the radial basis function network with tunable nodes
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Fast orthogonal least squares algorithm for efficient subset modelselection
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experiments with repeating weighted boosting search for optimization signal processing applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
RBFN restoration of nonlinearly degraded images
IEEE Transactions on Image Processing
An improved radial basis function network for visual autonomous road following
IEEE Transactions on Neural Networks
Genetic evolution of radial basis function coverage using orthogonal niches
IEEE Transactions on Neural Networks
A hybrid linear/nonlinear training algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Adaptive acquisition and tracking for deep space array feed antennas
IEEE Transactions on Neural Networks
A parameter optimization method for radial basis function type models
IEEE Transactions on Neural Networks
Clustering-based algorithms for single-hidden-layer sigmoid perceptron
IEEE Transactions on Neural Networks
On the construction and training of reformulated radial basis function neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Computers and Electrical Engineering
A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Twin least squares support vector regression
Neurocomputing
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We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.