label data meaning in Chinese
标号数据
Examples
- The algorithms of text classification are supervised , which means the classifier training need some human labeled data of fixed classes . generally , the accuracy of classifier is higher with more labeled data . but the labeled data by hand are expensive resource
文本分类算法是有监督的学习算法，它需要一个分类好的，类别已标识的文本数据集训练分类器，然后用训练好的分类器对未标识类别的文本分类。 - From another point , there are a great number of unlabeled documents available online . this paper approach to a novel algorithm , called iterative tfidf , which combines a large number of unlabeled data with small labeled data to train the tfidf classifier
网上存在大量文本，这些文本一般都没有类别标签，该算法可以利用大量廉价的未标识文本，结合很少的手工标识文本，通过迭代训练出较高精度的tfidf文本分类器。 - To extend chinese wall policy in multilevel security environment , authors use lattice to label data , and propose an improving policy in term of aggregate system . moreover , authors present a scheme using a database based history access and linklist of aggregate dataset of interest conflict
根据该环境中的chinese wall的利益冲突处理表现为数据聚合问题，利用数据标签的格级标定，提出一种基于历史访问库和利益冲突聚合链表的安全策略实现方法 - One vital problem with text classification is how to reduce the number of labeled data while maintain the proper accuracy . this paper partly solves this problem from two different aspects . firstly , we want to deal with sparse training data by selecting high performance algorithm
一般分类器的精度随着训练文本的增多而提高，但人工分类好的文本是一种昂贵的资源，文本分类算法要解决的一个重要问题是要减少训练集中人工分类的文本数量，同时保证其精度。 - Our research is about the classification problems on data with and without class labels attribute o classification with class label is mainly focus on dealing with noise , reconstruction of concept lattice , simplification of classification rules and a classification algorithm on class labeled data has been implemented
有类别属性的分类的研究的重点讨论数据噪声的处理、概念格的重构、分类规则的简化问题，并对其中的有确定类别属性的相关算法进行实现。