Combining active learning and relevance vector machines for text classification

机译：结合主动学习和相关矢量机器文本分类

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Relevance vector machines (RVM) have proven successful in many learning tasks. However, in large applications, they scale poorly. In many settings there is a large amount of unlabeled data which could be actively chosen by a learner and integrated in the

机译：相关矢量机（RVM）已被证明在许多学习任务中取得了成功。但是，在大型应用中，它们规模不佳。在许多设置中，有大量的未标记数据可以由学习者主动选择并集成在

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《International Conference on Machine Learning and Applications》|2007年||共6页
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作者
Silva C.; Ribeiro B.;
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中图分类 TP18-53;
关键词
learning (artificial intelligence); pattern classification; support vector machines; text analysis; Reuters-21578 benchmark; active learning; data categorization; kernel distance metrics; kernel trick; relevance vector machines; text classification;

机译：学习（人工智能）;模式分类;支持向量机;文本分析;Reuters-21578基准;主动学习;数据分类;核心分类;内核距离指标;核心;相关矢量机;文本分类;

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2. Support Vector Machine Active Learning with Applications to Text Classification [J] . Tong Simon, Koller Daphne Journal of machine learning research . 2001,第Nov期

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4. Combining active learning and relevance vector machines for text classification [C] . Silva C., Ribeiro B. International Conference on Machine Learning and Applications . 2007

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7. Semi-supervised learning combining transductive support vector machine with active learning [O] . Boli Lu, Xibin Wang 2015

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Combining active learning and relevance vector machines for text classification

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