Paper Info
Title
Improving Classification with Pairwise Constraints: A Margin-Based Approach
Abstract
In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficient algorithm, PCSVM, to solve the pairwise constraint learning problem. Experiments with 15 data sets show that pairwise constraint information significantly increases the performance of classification.
Year
DOI
Venue
2008
10.1007/978-3-540-87481-2_8
ECML/PKDD
Keywords
Field
DocType
pairwise constraints,small amount,improving classification,semi-supervised learning problem,conventional margin-based learning framework,different class,margin-based approach,pairwise constraint,efficient algorithm,pairwise constraint information,semi supervised learning,classification,discrimination learning
Pairwise comparison,Data set,Semi-supervised learning,Stability (learning theory),Active learning (machine learning),Unsupervised learning,Constraint learning,Artificial intelligence,Labeled data,Machine learning,Mathematics
Conference
Volume
ISSN
Citations 
5212
0302-9743
13
PageRank 
References 
Authors
0.62
17
2
Name
Order
Citations
PageRank
Nam Nguyen133116.64
Rich Caruana24503655.71