BoosTexter: A boosting-based system for text categorization
BoosTexter: A boosting-based system for text categorization
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DOI：
10.1023/a:1007649029923
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发表时间：
2000-05-01
期刊：
影响因子：
7.5
通讯作者：
Singer, Y
中科院分区：
文献类型：
--
作者：
Schapire, RE;Singer, Y
这项工作的重点是算法，从例子中学习，执行多类文本和语音分类任务。我们的方法是基于一个新的和改进的家庭的升压算法。我们详细描述了一个实现，称为BoosTexter，新的提升算法的文本分类任务。我们目前的结果比较性能的BoosTexter和其他一些文本分类算法的各种任务。最后，我们描述了我们的系统的应用程序，自动呼叫类型识别不受约束的口头客户响应。
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.