The annealing algorithm
A simple Wilson orthonormal basis with exponential decay
SIAM Journal on Mathematical Analysis
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Orthonormal ridgelets and linear singularities
SIAM Journal on Mathematical Analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Wavelet support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
Neurocomputing
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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A ridgelet kernel regression method is presented in this paper to approximate multi-dimensional functions, especially those with certain kinds of spatial inhomogeneities. This method is based on ridgelet theory, kernel and regularization techniques from which we can deduce a regularized kernel regression form. By representing this form with quadratic programming and taking the obtained solution to define a fitness function, we use particle swarm optimization to optimize the directions of ridgelets. The properties of ridgelet can guarantee the stability of this method in approximating multi-dimensional functions, as well as its superiority for functions with linear singularities. Additionally, the regularized technique employed in this model leads to smaller generalization error. Experiments in the tasks of regression and classification show its effectiveness.