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Knowledge Transferring Via Implicit Link Analysis Xiao Ling, Wenyuan Dai, Gui-Rong Xue, and Yong Yu
 

Summary: Knowledge Transferring Via Implicit Link Analysis
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, and Yong Yu
Department of Computer Science and Engineering
Shanghai Jiao Tong University
No. 800 Dongchuan Road, Shanghai 200240, China
{shawnling,dwyak,grxue,yyu}@apex.sjtu.edu.cn
Abstract. In this paper, we design a local classification algorithm using implicit
link analysis, considering the situation that the labeled and unlabeled data are
drawn from two different albeit related domains. In contrast to many global clas-
sifiers, e.g. Support Vector Machines, our local classifier only takes into account
the neighborhood information around unlabeled data points, and is hardly based
on the global distribution in the data set. Thus, the local classifier has good abil-
ities to tackle the non-i.i.d. classification problem since its generalization will
not degrade by the bias w.r.t. each unlabeled data point. We build a local neigh-
borhood by connecting the similar data points. Based on these implicit links, the
Relaxation Labeling technique is employed. In this work, we theoretically and
empirically analyze our algorithm, and show how our algorithm improves the
traditional classifiers. It turned out that our algorithm greatly outperforms the
state-of-the-art supervised and semi-supervised algorithms when classifying doc-
uments across different domains.

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences