Stanford InfoLab Publication Server

Using Feature Conjunctions across Examples for Learning Pairwise Classifiers

Oyama, Satoshi and Manning, Christopher D. (2004) Using Feature Conjunctions across Examples for Learning Pairwise Classifiers. Technical Report. Stanford.

WarningThere is a more recent version of this item available.



We propose a kernel method for using combinations of features across example pairs in learning pairwise classifiers. Identifying two instances in the same class is an important technique in duplicate detection, entity matching, and other clustering problems. However, it is a difficult problem when instances have few discriminative features. One typical example is to check whether two abbreviated author names in different papers refer to the same person or not. While using combinations of different features from each instance may improve the classification accuracy, doing this straightforwardly is computationally intensive. Our method uses interaction between different features without high computational cost using a kernel. At medium recall levels, this method can give a precision 4 to 8 times higher than that of previous methods in author matching problems.

Item Type:Techreport (Technical Report)
Uncontrolled Keywords:Kernel Methods, Support Vector Machines, Pairwise Classification, Learning Similarity Measures, Entity Matching, Duplicate Detection, Clustering
Subjects:Computer Science
Computer Science > Data Mining
Computer Science > Data Integration and Mediation
Related URLs:Project Homepage, Project Homepage,
ID Code:648
Deposited By:Import Account
Deposited On:27 Apr 2004 17:00
Last Modified:23 Dec 2008 09:42

Available Versions of this Item

Download statistics

Repository Staff Only: item control page