Add to ORKGAbstract. A formal model of machine learning by considering user preference of attributes is proposed in this paper. The model seamlessly combines internal information and external information. This model can be extended to user preference of attribute sets. By using the user preference of attribute sets, user preferred reducts can be constructed.
One approach to preference learning, based on linear support vector machines, involves choosing a we...
P&al031International audienceThis editorial of the special issue "Representing, Processing, and Lear...
Abstract. This paper presents most used user models and inductive methods based on probability. It d...
The effectiveness of any machine learning algorithm depends, to a large extent, on the selection of ...
In this thesis we address to the problematics of modelling user preferences. We discuss different vi...
Abstract. A relaxed setting for Feature Selection is known as Feature Ranking in Machine Learning. T...
Learning of preference relations has recently received significant attention in machine learning com...
This dissertation focuses on developing new machine learning models and algorithms for the task of l...
Recently, a lot of interest arose in the artificial intelligence and database communities concerning...
This paper focuses to a formal model of user preference learning for content-based recommender syste...
In previous work, we introduced a representation language, DD-PREF, that balances preferences for pa...
AbstractWe propose a new method for modelling users' preferences on attributes that contain more tha...
Abstract.1 One of the most challenging goals of recommender systems is to infer the preferences of u...
Abstract. Nowadays modeling user’s preferences is one of the most challenging tasks in e-learning sy...
Abstract. Recommender systems suggest users information items they may be interested in. User profil...
One approach to preference learning, based on linear support vector machines, involves choosing a we...
P&al031International audienceThis editorial of the special issue "Representing, Processing, and Lear...
Abstract. This paper presents most used user models and inductive methods based on probability. It d...
The effectiveness of any machine learning algorithm depends, to a large extent, on the selection of ...
In this thesis we address to the problematics of modelling user preferences. We discuss different vi...
Abstract. A relaxed setting for Feature Selection is known as Feature Ranking in Machine Learning. T...
Learning of preference relations has recently received significant attention in machine learning com...
This dissertation focuses on developing new machine learning models and algorithms for the task of l...
Recently, a lot of interest arose in the artificial intelligence and database communities concerning...
This paper focuses to a formal model of user preference learning for content-based recommender syste...
In previous work, we introduced a representation language, DD-PREF, that balances preferences for pa...
AbstractWe propose a new method for modelling users' preferences on attributes that contain more tha...
Abstract.1 One of the most challenging goals of recommender systems is to infer the preferences of u...
Abstract. Nowadays modeling user’s preferences is one of the most challenging tasks in e-learning sy...
Abstract. Recommender systems suggest users information items they may be interested in. User profil...
One approach to preference learning, based on linear support vector machines, involves choosing a we...
P&al031International audienceThis editorial of the special issue "Representing, Processing, and Lear...
Abstract. This paper presents most used user models and inductive methods based on probability. It d...