分类: 计算机科学 >> 计算机科学技术其他学科 提交时间: 2019-03-17
摘要: Traditional semi-supervised learning uses only labeled instances to train a classifier and then this classifier is utilized to classify unlabeled instances, while sometimes there are only positive instances which are elements of the target concept are available in the labeled set. Our research in this paper the design of learning algorithms from positive and unlabeled instances only. Among all the semi-supervised positive and unlabeled learning methods, it is a fundamental step to extract useful information from unlabeled instances. In this paper, we design a novel framework to take advantage of valid information in unlabeled instances. In essence, this framework mainly includes that (1) selects reliable negative instances through the fuzziness of the instances; (2) chooses new positive instances based on the fuzziness of the instances to expand the initial positive set, and we named these new instances as reliable positive instances; (3) uses data editing technique to filter out noise points with high fuzziness. The effectiveness of the presented algorithm is verified by comparative experiments on UCI dataset.
分类: 图书馆学、情报学 >> 图书馆学 提交时间: 2019-03-17
摘要: 文梳理了科技预印本库的国际发展趋势;从国际重要预印本库自身、科研基金为代表的科技管理部门、以及科技期刊三个方面,分析了当前预印本交流的相关政策动向;研究提出了我国科技预印本库建设中面临的五个政策挑战:政策定位不清晰,政策机制不完善、高层管理政策缺失、得不到期刊出版政策支持、政策起点高度不够;最后提出了发展我国科技预印本交流体系的四条政策建议。