Local Minimax Learning of Functions With Best Finite Sample Estimation Error Bounds: Applications to Ridge and Lasso Regression, Boosting, Tree Learning, Kernel Machines, and Inverse Problems

Optimal local estimation is formulated in the minimax sense for inverse problems and nonlinear regression. This theory provides best mean squared finite sample error bounds for some popular statistical learning algorithms and also for several optimal improvements of other existing learning algorithm...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 55; H. 12; S. 5700 - 5727
1. Verfasser:	Jones, L.K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.12.2009 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Boosting Errors Estimating techniques Estimation error Exact sciences and technology Fusion Information geometry Information theory Information, signal and communications theory inverse problem Inverse problems Kernel Learning Machine learning Mathematical analysis Mathematical models minimax Minimax technique Minimax techniques Parameter optimization Pattern recognition Regression analysis Regression tree analysis reproducing kernel ridge regression Samples Signal processing Smoothing methods Statistical analysis Statistical learning Statistical methods Telecommunications and information theory Validity Query Optimal estimation Modeling Implementation Spline Mean square error Learning Optimal strategy Hilbert space Learning algorithm minimax Estimation error Smoothing methods Non linear regression ridge regression Regression analysis Pattern recognition Quadratic programming reproducing kernel Kernel method Inverse problem Pattern classification Minimax method Metric Fusion Algebraic geometry
ISSN:	0018-9448, 1557-9654
Online-Zugang:	Volltext
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