Add to ORKGWe determine the asymptotic limit of the function computed by support vector machines (SVM) and related algorithms that minimize a regu-larized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is held fixed. Non-asymptotic con-vergence bounds to this limit in the L2 sense are provided, together with upper bounds on the classification error that is shown to converge to the Bayes risk, therefore proving the Bayes-consistency of a variety of meth-ods although the regularization term does not vanish. These results are particularly relevant to the one-...
We present a globally convergent method for regularized risk minimization prob-lems. Our method appl...
We present a globally convergent method for regularized risk minimization problems. Our method appli...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
AbstractIn nonparametric classification and regression problems, regularized kernel methods, in part...
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subse...
The decision functions constructed by support vector machines (SVM's) usually depend only on a...
We define notions of stability for learning algorithms and show how to use these notions to derive g...
We define notions of stability for learning algorithms and show how to use these notions to derive g...
We define notions of stability for learning algorithms and show how to use these notions to derive g...
AbstractA family of classification algorithms generated from Tikhonov regularization schemes are con...
Support vector machines (SVM's) construct decision functions that are linear combinations of k...
Regularized kernel methods such as, e.g., support vector machines and least-squares support vector r...
AbstractThe classical support vector machines regression (SVMR) is known as a regularized learning a...
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKH...
The support vector machine methodology is a rapidly growing area of research in machine learning. A ...
We present a globally convergent method for regularized risk minimization prob-lems. Our method appl...
We present a globally convergent method for regularized risk minimization problems. Our method appli...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
AbstractIn nonparametric classification and regression problems, regularized kernel methods, in part...
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subse...
The decision functions constructed by support vector machines (SVM's) usually depend only on a...
We define notions of stability for learning algorithms and show how to use these notions to derive g...
We define notions of stability for learning algorithms and show how to use these notions to derive g...
We define notions of stability for learning algorithms and show how to use these notions to derive g...
AbstractA family of classification algorithms generated from Tikhonov regularization schemes are con...
Support vector machines (SVM's) construct decision functions that are linear combinations of k...
Regularized kernel methods such as, e.g., support vector machines and least-squares support vector r...
AbstractThe classical support vector machines regression (SVMR) is known as a regularized learning a...
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKH...
The support vector machine methodology is a rapidly growing area of research in machine learning. A ...
We present a globally convergent method for regularized risk minimization prob-lems. Our method appl...
We present a globally convergent method for regularized risk minimization problems. Our method appli...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...