判别式
支持向量机
边距(机器学习)
水准点(测量)
机器学习
等级制度
任务(项目管理)
人工智能
模式识别(心理学)
计算机科学
工程类
地理
大地测量学
系统工程
经济
市场经济
作者
Shantanu Godbole,Sunita Sarawagi
标识
DOI:10.1007/978-3-540-24775-3_5
摘要
In this paper we present methods of enhancing existing discriminative classifiers for multi-labeled predictions. Discriminative methods like support vector machines perform very well for uni-labeled text classification tasks. Multi-labeled classification is a harder task subject to relatively less attention. In the multi-labeled setting, classes are often related to each other or part of a is-a hierarchy. We present a new technique for combining text features and features indicating relationships between classes, which can be used with any discriminative algorithm. We also present two enhancements to the margin of SVMs for building better models in the presence of overlapping classes. We present results of experiments on real world text benchmark datasets. Our new methods beat accuracy of existing methods with statistically significant improvements.
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