2001/06/20 by Yair Even-Zohar, Dan Roth, Even-Zohar, Yair +1
Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #I.2.6 #I.2.67 #Machine Learning (cs.LG) #Time Series Analysis and Forecasting #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.cs/0106044
arxiv created 2001/06/20 · openalex publication_date 2001/06/20 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach -- a sequential learning model that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probability, the presence of the true outcome in the candidates set. Some theoretical and computational properties of the model are discussed and we argue that these are important in NLP-like domains. The advantages of the model are illustrated in an experiment in part-of-speech tagging.