Kernel least-squares models using updates of the pseudoinverse

Authors:
E. Andelić;M. Schafföner;M. Katz;S. E. Krüger;A. Wendemuth
Affiliations:
Cognitive Systems Group, IESK, Otto-von-Guericke-University, Magdeburg, Germany;Cognitive Systems Group, IESK, Otto-von-Guericke-University, Magdeburg, Germany;Cognitive Systems Group, IESK, Otto-von-Guericke-University, Magdeburg, Germany;Cognitive Systems Group, IESK, Otto-von-Guericke-University, Magdeburg, Germany;Cognitive Systems Group, IESK, Otto-von-Guericke-University, Magdeburg, Germany
Venue:
Neural Computation
Year:
2006

Citing 8
Cited 3

Sparse Approximate Solutions to Linear Systems

SIAM Journal on Computing
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Soft Margins for AdaBoost

Machine Learning
Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm

Neural Networks
Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel fisher discriminant analysis

Neural Computation
Sparse Greedy Matrix Approximation for Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Some greedy learning algorithms for sparse regression and classification with mercer kernels

The Journal of Machine Learning Research
Pruning error minimization in least squares support vector machines

IEEE Transactions on Neural Networks

Adaptive kernel canonical correlation analysis algorithms for nonparametric identification of Wiener and Hammerstein systems

EURASIP Journal on Advances in Signal Processing
Sparse approximation through boosting for learning large scale kernel machines

IEEE Transactions on Neural Networks
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sparse nonlinear classification and regression models in reproducing kernel Hilbert spaces (RKHSs) are considered. The use of Mercer kernels and the square loss function gives rise to an overdetermined linear least-squares problem in the corresponding RKHS. When we apply a greedy forward selection scheme, the least-squares problem may be solved by an order-recursive update of the pseudoinverse in each iteration step. The computational time is linear with respect to the number of the selected training samples.