A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Ten lectures on wavelets
Regularization theory and neural networks architectures
Neural Computation
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An equivalence between sparse approximation and support vector machines
Neural Computation
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Wavelet kernel penalized estimation for non-equispaced design regression
Statistics and Computing
Expert Systems with Applications: An International Journal
Representing functional data using support vector machines
Pattern Recognition Letters
A pattern recognition based approach to consistency analysis of geophysical datasets
Computers & Geosciences
Deriving the kernel from training data
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Scaling kernels: a new least squares support vector machine kernel for approximation
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Framelet kernels with applications to support vector regression and regularization networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
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This work deals with a method for building a reproducing kernel Hilbert space (RKHS) from a Hilbert space with frame elements having special properties. Conditions on existence and a method of construction are given. Then, these RKHS are used within the framework of regularization theory for function approximation. Implications on semiparametric estimation are discussed and a multiscale scheme of regularization is also proposed. Results on toy and real-world approximation problems illustrate the effectiveness of such methods.