Customer realized feature ranking.
客户认识到的特性分级。
Since the data samples in machine learning and pattern recognition problems generally distribute in multi-modal distribution, this thesis proposed a prototype based feature ranking model.
由于模式识别、机器学习等问题的复杂性比较高，数据分布通常呈现多模态分布。
Then we sort these feature subsets according to the ranking performances.
然后根据排序精度排列这些特征子集。
This paper presents a method of neural networks feature selection based on data attributes importance ranking.
提出一种基于数据属性重要性排序的神经网络属性选择方法。
Based on the existing ranking algorithms, website features were introduced, and a novel ranking algorithm based on website feature identification was proposed.
本文在现有排序算法的基础上引入网站特征，提出了一种基于网站特征识别的搜索引擎排序算法。
Then, this similarity score is used as a new feature for answer ranking in open domain question answering (QA) track.
这种匹配得分被作为新的特征，应用于计算答案的置信度之中。
Then, this similarity score is used as a new feature for answer ranking in open domain question answering (QA) track.
这种匹配得分被作为新的特征，应用于计算答案的置信度之中。
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