AbstractA family of classification algorithms generated from Tikhonov regularization schemes are considered. They involve multi-kernel spaces and general convex loss functions. Our main purpose is to provide satisfactory estimates for the excess misclassification error of these multi-kernel regularized classifiers when the loss functions achieve the zero value. The error analysis consists of two parts: regularization error and sample error. Allowing multi-kernels in the algorithm improves the regularization error and approximation error, which is one advantage of the multi-kernel setting. For a general loss function, we show how to bound the regularization error by the approximation in some weighted Lq spaces. For the sample error, we use a...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2002.Includes bi...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
AbstractRegularized classifiers (a leading example is support vector machine) are known to be a kind...
AbstractIn this paper we consider fully online learning algorithms for classification generated from...
Regularized classifiers (a leading example is support vector machine) are known to be a kind of kern...
AbstractRegularized classifiers (a leading example is support vector machine) are known to be a kind...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
AbstractIn this paper we consider fully online learning algorithms for classification generated from...
We determine the asymptotic limit of the function computed by support vector machines (SVM) and rela...
We consider the solution of binary classification problems via Tikhonov regularization in a Reprodu...
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature...
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKH...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2002.Includes bi...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
AbstractRegularized classifiers (a leading example is support vector machine) are known to be a kind...
AbstractIn this paper we consider fully online learning algorithms for classification generated from...
Regularized classifiers (a leading example is support vector machine) are known to be a kind of kern...
AbstractRegularized classifiers (a leading example is support vector machine) are known to be a kind...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
AbstractIn this paper we consider fully online learning algorithms for classification generated from...
We determine the asymptotic limit of the function computed by support vector machines (SVM) and rela...
We consider the solution of binary classification problems via Tikhonov regularization in a Reprodu...
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature...
The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKH...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2002.Includes bi...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...