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Regularized least squares

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the… 
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Papers overview

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2018
2018
Graph based Semi-Supervised Learning (G-SSL) methods usually include the stages of the construction of affinity matrix and the… 
2015
2015
krls implements Kernel-Based Regularized Least Squares (KRLS), a machine learning method described in Hainmueller and Hazlett… 
2013
2013
We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows… 
2011
2011
Cet article traite du probleme de classification multi-classe en reconnaissance des formes. La resolution de ce type de problemes… 
2010
2010
A Synthetic Aperture Radar (SAR) target recognition approach based on KPCA (kernel principal component analysis) and Laplacian… 
2009
2009
This paper discusses the undervalued importance of Regularized Least Squares, and its continued usefulness in solving supervised… 
2006
2006
In this paper, we propose a regularized least squares approach to potential SVRs. The proposed solution involves inverting a… 
2005
2005
We discuss stability for a class of learning algorithms with respect to noisy labels. The algorithms we consider are for… 
2005
2005
We consider regularized least-squares (RLS) with a Gaussian kernel. We prove that if we let the Gaussian bandwidth σ → ∞ while… 
Review
2004
Review
2004
We present a survey of recent results concerning the theoretical and empirical performance of algorithms for learning regularized…