Add to ORKGMany interesting multiclass problems can be cast in the general frame- work of label ranking defined on a given set of classes. The evaluation for such a ranking is generally given in terms of the number of violated order constraints between classes. In this paper, we propose the Prefer- ence Learning Model as a unifying framework to model and solve a large class of multiclass problems in a large margin perspective. In addition, an original kernel-based method is proposed and evaluated on a ranking dataset with state-of-the-art results
Learning of preference relations has recently received significant attention in machine learning com...
AbstractWe study the problem of label ranking, a machine learning task that consists of inducing a m...
There are many applications in which it is desirable to order rather than classify instances. Here w...
Many interesting multiclass problems can be cast in the general frame- work of label ranking defined...
Despite that the majority of machine learning approaches aim to solve binary classification problems...
Learning preferences between objects constitutes a challenging task that notably differs from standa...
Abstract. Situations when only a limited amount of labeled data and a large amount of unlabeled data...
Preference learning is a challenging problem that involves the prediction of complex structures, suc...
Supervised learning is characterized by a broad spectrum of learning problems, often involving struc...
The preference model introduced in this paper gives a natural framework and a principled solution fo...
While most existing multilabel ranking methods assume the availability of a single objective label r...
The preference model introduced in this paper gives a natural framework and a principled solution fo...
AbstractPreference learning is an emerging topic that appears in different guises in the recent lite...
The constraint classification framework captures many flavors of multiclass classification including...
Conventional classification learning allows a classifier to make a one shot decision in order to ide...
Learning of preference relations has recently received significant attention in machine learning com...
AbstractWe study the problem of label ranking, a machine learning task that consists of inducing a m...
There are many applications in which it is desirable to order rather than classify instances. Here w...
Many interesting multiclass problems can be cast in the general frame- work of label ranking defined...
Despite that the majority of machine learning approaches aim to solve binary classification problems...
Learning preferences between objects constitutes a challenging task that notably differs from standa...
Abstract. Situations when only a limited amount of labeled data and a large amount of unlabeled data...
Preference learning is a challenging problem that involves the prediction of complex structures, suc...
Supervised learning is characterized by a broad spectrum of learning problems, often involving struc...
The preference model introduced in this paper gives a natural framework and a principled solution fo...
While most existing multilabel ranking methods assume the availability of a single objective label r...
The preference model introduced in this paper gives a natural framework and a principled solution fo...
AbstractPreference learning is an emerging topic that appears in different guises in the recent lite...
The constraint classification framework captures many flavors of multiclass classification including...
Conventional classification learning allows a classifier to make a one shot decision in order to ide...
Learning of preference relations has recently received significant attention in machine learning com...
AbstractWe study the problem of label ranking, a machine learning task that consists of inducing a m...
There are many applications in which it is desirable to order rather than classify instances. Here w...