首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
【24h】

A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets

机译:从大规模数据集中学习排名函数的快速算法

获取原文
获取原文并翻译 | 示例

摘要

We consider the problem of learning the ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an $epsilon$-accurate approximation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from $mathcal{O}(m^2)$, to $mathcal{O}(m)$, where $m$ is the number of training samples. Experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy, when the algorithms are trained on the same data. However, since it is several orders of magnitude faster than the current state-of-the-art approaches, it is able to leverage much larger training datasets.
机译:我们考虑了学习排序函数的问题,该函数最大化了训练数据上的Wilcoxon-Mann-Whitney统计量的最大化。依靠误差函数的$ε精确逼近,我们将用于学习排名函数的共轭梯度算法的每次迭代的计算复杂度从$ mathcal {O}(m ^ 2)$降低到$ mathcal {O }(m)$,其中$ m $是训练样本的数量。在序数回归和协同过滤的公共基准上进行的实验表明,在对相同数据进行训练时,就排名准确性而言,该算法与最佳可用方法一样准确。但是,由于它比当前的最新技术快几个数量级,因此它能够利用更大的训练数据集。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号