A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Representation and learning in information retrieval
Representation and learning in information retrieval
Machine Learning - Special issue on inductive transfer
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Graph-based text classification: learn from your neighbors
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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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 classifiers, 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 abilities 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 neighborhood 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 documents across different domains.