NDLI: trNon-greedy active learning for text categorization using convex ansductive experimental design

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trNon-greedy active learning for text categorization using convex ansductive experimental design

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trNon-greedy active learning for text categorization using convex ansductive experimental design

Content Provider	ACM Digital Library
Author	Gong, Yihong Yu, Kai Zhu, Shenghuo Xu, Wei
Abstract	In this paper we propose a non-greedy active learning method for text categorization using least-squares support vector machines (LSSVM). Our work is based on transductive experimental design (TED), an active learning formulation that effectively explores the information of unlabeled data. Despite its appealing properties, the optimization problem is however NP-hard and thus--like most of other active learning methods--a greedy sequential strategy to select one data example after another was suggested to find a suboptimum. In this paper we formulate the problem into a continuous optimization problem and prove its convexity, meaning that a set of data examples can be selected with a guarantee of global optimum. We also develop an iterative algorithm to efficiently solve the optimization problem, which turns out to be very easy-to-implement. Our text categorization experiments on two text corpora empirically demonstrated that the new active learning algorithm outperforms the sequential greedy algorithm, and is promising for active text categorization applications.
Starting Page	635
Ending Page	642
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390442
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Transductive experimental design Active learning Convex optimization Text categorization
Content Type	Text
Resource Type	Article

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