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

页码：899 / 908

页数：10

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