Three estimates of the leave-one-out error for nu-support vector (SV) machine binary classifiers are presented. Two of the estimates are based on the geometrical concept of the em span, which was introduced in the context of bounding the leave-one-out error for C-SV machine binary classifiers, while the third is based on optimisation over the criterion used to train the nu-support vector classifier. It is shown that the estimates presented herein provide informative and efficient approximations of the generalisation behaviour, in both a toy example and benchmark data sets. The proof strategies in the nu-SV context are also compared with those used to derive leave-one-out error estimates in the C-SV case
Motivated by the Golub-Heath-Wahba formula for ridge regression, we first present a new leave-one-ou...
Dual-nu Support Vector Machine (SVM) is an effective method in pattern recognition and target detect...
Abstract A unified view on multi-class support vector machines (SVMs) is presented, covering most pr...
Three estimates of the leave-one-out error for nu-support vector (SV) machine binary classifiers are...
Abstract Three estimates of the leave-one-out error for *-support vector (SV) machine binary classif...
Abstract The selection of parameters in the support vector machine (SVM) is an important step for co...
We present a new learning algorithm for pat-tern recognition inspired by a recent upper bound on lea...
We propose an algorithm to predict the leave-one-out (LOO) error for kernel based classifiers. To ac...
We study the leave-one-out and generalization errors of voting combinations of learning machines. A ...
Tech ReportStandard classification algorithms aim to minimize the probability of making an incorrect...
Abstract. We study the leave-one-out and generalization errors of voting combinations of learning ma...
Minimizing bounds of leave-one-out (loo) errors is an important & efficient approach for support vec...
The Support Vector Machine (SVM) is a binary classification paradigm based on statistical learning. ...
International audienceTo set the values of the hyperparameters of a support vector machine (SVM), on...
In binary classification there are two types of errors, and in many applications these may have very...
Motivated by the Golub-Heath-Wahba formula for ridge regression, we first present a new leave-one-ou...
Dual-nu Support Vector Machine (SVM) is an effective method in pattern recognition and target detect...
Abstract A unified view on multi-class support vector machines (SVMs) is presented, covering most pr...
Three estimates of the leave-one-out error for nu-support vector (SV) machine binary classifiers are...
Abstract Three estimates of the leave-one-out error for *-support vector (SV) machine binary classif...
Abstract The selection of parameters in the support vector machine (SVM) is an important step for co...
We present a new learning algorithm for pat-tern recognition inspired by a recent upper bound on lea...
We propose an algorithm to predict the leave-one-out (LOO) error for kernel based classifiers. To ac...
We study the leave-one-out and generalization errors of voting combinations of learning machines. A ...
Tech ReportStandard classification algorithms aim to minimize the probability of making an incorrect...
Abstract. We study the leave-one-out and generalization errors of voting combinations of learning ma...
Minimizing bounds of leave-one-out (loo) errors is an important & efficient approach for support vec...
The Support Vector Machine (SVM) is a binary classification paradigm based on statistical learning. ...
International audienceTo set the values of the hyperparameters of a support vector machine (SVM), on...
In binary classification there are two types of errors, and in many applications these may have very...
Motivated by the Golub-Heath-Wahba formula for ridge regression, we first present a new leave-one-ou...
Dual-nu Support Vector Machine (SVM) is an effective method in pattern recognition and target detect...
Abstract A unified view on multi-class support vector machines (SVMs) is presented, covering most pr...