BoosTexter: A boosting-based system for text categorization

作     者：Schapire, RE Singer, Y 

作者机构：AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel 

出 版 物：《MACHINE LEARNING》 (Mach Learn)

年 卷 期：2000年第39卷第2-3期

页      面：135-168页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：text and speech categorization multiclass classification problems boosting algorithms 

摘      要：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.