Add to ORKGWe present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived
Margin maximizing properties play an important role in the analysis of classification models, such ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Model selection in Support Vector machines is usually carried out by minimizing the quotient of the ...
Typical bounds on generalization of Support Vector Machines are based on the minimum distance betwee...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Generalization bounds depending on the margin of a classifier are a relatively recent development. T...
We apply methods of Statistical Mechanics to study the generalization performance of Support vector ...
A number of results have bounded generalization of a classifier in terms of its margin on the traini...
Consider the problem of learning a kernel for use in SVM classification. We bound the estimation err...
We present a bound on the generalisation error of linear classifiers in terms of a refined margin qu...
Anumber of results have bounded generalization of a classi er in terms of its margin on the training...
A number of results have bounded generalization error of a classifier in terms of its margin on the ...
Abstract—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane ...
In many classification procedures, the classification function is obtained (or trained) by minimizi...
Margin maximizing properties play an important role in the analysis of classification models, such ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Model selection in Support Vector machines is usually carried out by minimizing the quotient of the ...
Typical bounds on generalization of Support Vector Machines are based on the minimum distance betwee...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Generalization bounds depending on the margin of a classifier are a relatively recent development. T...
We apply methods of Statistical Mechanics to study the generalization performance of Support vector ...
A number of results have bounded generalization of a classifier in terms of its margin on the traini...
Consider the problem of learning a kernel for use in SVM classification. We bound the estimation err...
We present a bound on the generalisation error of linear classifiers in terms of a refined margin qu...
Anumber of results have bounded generalization of a classi er in terms of its margin on the training...
A number of results have bounded generalization error of a classifier in terms of its margin on the ...
Abstract—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane ...
In many classification procedures, the classification function is obtained (or trained) by minimizi...
Margin maximizing properties play an important role in the analysis of classification models, such ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Model selection in Support Vector machines is usually carried out by minimizing the quotient of the ...