DOIAbstract
We survey a number of recent results concerning the behaviour of algorithms for learning classifiers based on the solution of a regularized least-squares problem
We survey a number of recent results concerning the behaviour of algorithms for learning classifiers based on the solution of a regularized least-squares problem
We develop a theoretical analysis of generalization performances of regularized least-squares on rep...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification...
AbstractWe survey a number of recent results concerning the behaviour of algorithms for learning cla...
We consider the solution of binary classification problems via Tikhonov regularization in a Reprodu...
Abstract—The regularized least-squares classification is one of the most promising alternatives to s...
We investigate the problem of model selection for learning algorithms depending on a continuous para...
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Compute...
We develop a theoretical analysis of the generalization perfor- mances of regularized least-squares ...
We develop a theoretical analysis of the generalization perfor-mances of regularized least-squares a...
In this work we present the first efficient algorithm for unsupervised training of multi-class re...
Over the past decades, regularization theory is widely applied in various areas of machine learning ...
Over the past decades, regularization theory is widely applied in various areas of machine learning ...
Abstract. We provide sample complexity of the problem of learning halfspaces with monotonic noise, u...
We revisit the classical technique of regularised least squares (RLS) for nonlinear classification i...
We develop a theoretical analysis of generalization performances of regularized least-squares on rep...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification...
AbstractWe survey a number of recent results concerning the behaviour of algorithms for learning cla...
We consider the solution of binary classification problems via Tikhonov regularization in a Reprodu...
Abstract—The regularized least-squares classification is one of the most promising alternatives to s...
We investigate the problem of model selection for learning algorithms depending on a continuous para...
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Compute...
We develop a theoretical analysis of the generalization perfor- mances of regularized least-squares ...
We develop a theoretical analysis of the generalization perfor-mances of regularized least-squares a...
In this work we present the first efficient algorithm for unsupervised training of multi-class re...
Over the past decades, regularization theory is widely applied in various areas of machine learning ...
Over the past decades, regularization theory is widely applied in various areas of machine learning ...
Abstract. We provide sample complexity of the problem of learning halfspaces with monotonic noise, u...
We revisit the classical technique of regularised least squares (RLS) for nonlinear classification i...
We develop a theoretical analysis of generalization performances of regularized least-squares on rep...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification...
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