NDLI: Hierarchical document categorization with support vector machines

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Hierarchical document categorization with support vector machines

Content Provider	ACM Digital Library
Author	Hofmann, Thomas Cai, Lijuan
Abstract	Automatically categorizing documents into pre-defined topic hierarchies or taxonomies is a crucial step in knowledge and content management. Standard machine learning techniques like Support Vector Machines and related large margin methods have been successfully applied for this task, albeit the fact that they ignore the inter-class relationships. In this paper, we propose a novel hierarchical classification method that generalizes Support Vector Machine learning and that is based on discriminant functions that are structured in a way that mirrors the class hierarchy. Our method can work with arbitrary, not necessarily singly connected taxonomies and can deal with task-specific loss functions. All parameters are learned jointly by optimizing a common objective function corresponding to a regularized upper bound on the empirical loss. We present experimental results on the WIPO-alpha patent collection to show the competitiveness of our approach.
Starting Page	78
Ending Page	87
Page Count	10
File Format	PDF
ISBN	1581138741
DOI	10.1145/1031171.1031186
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-11-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hierarchical loss Class relationship Taxonomy Svm Subspace optimization Document categorization
Content Type	Text
Resource Type	Article

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