Label ranking by learning pairwise preferences
نویسندگان
چکیده
منابع مشابه
Label Ranking by Learning Pairwise Preferences Label Ranking by Learning Pairwise Preferences
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to...
متن کاملLabel ranking by learning pairwise preferences
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn ...
متن کاملPreference Learning and Ranking by Pairwise Comparison
This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction prob...
متن کاملLabelwise versus Pairwise Decomposition in Label Ranking
Label ranking is a specific type of preference learning problem, namely the problem of learning a model that maps instances to rankings over a finite set of predefined alternatives (labels). State-of-the-art approaches to label ranking include decomposition techniques that reduce the original problem to binary classification; ranking by pairwise comparison (RPC), for example, constructs one bin...
متن کاملLearning Label Preferences: Ranking Error Versus Position Error
We consider the problem of learning a ranking function, that is a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by pairwise comparison (RPC), first induces pairwise order relations from suitable training data, using a natural extension of so-called pairwise classification. A ranking is then derived from a set of such relations by ...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2008
ISSN: 0004-3702
DOI: 10.1016/j.artint.2008.08.002