A neural network approach for learning object ranking

被引：0
|
作者：
Rigutini, Leonardo [1 ]
Papini, Tiziano [1 ]
Maggini, Marco [1 ]
Bianchini, Monica [1 ]
机构：
[1] Univ Siena, Dipartimento Ingn Informaz, Siena, Italy
来源：
ARTIFICIAL NEURAL NETWORKS - ICANN 2008, PT II | 2008年 / 5164卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we present a connectionist approach to preference learning. In particular, a neural network is trained to realize a comparison function, expressing the preference between two objects. Such a "comparator" can be subsequently integrated into a general ranking algorithm to provide a total ordering on some collection of objects. We evaluate the accuracy of the proposed approach using the LETOR. benchmark, with promising preliminary results.
引用
收藏
页码：899 / 908
页数：10
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