Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discovering Test Set Regularities in Relational Domains
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Trans-Media Pseudo-Relevance Feedback Methods in Multimedia Retrieval
Advances in Multilingual and Multimodal Information Retrieval
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Automatic foldering of email messages: a combination approach
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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Most of the approaches in multi-view categorization use early fusion, late fusion or co-training strategies. We propose here a novel classification method that is able to efficiently capture the interactions across the different modes. This method is a multi-modal extension of the Rocchio classification algorithm --- very popular in the Information Retrieval community. The extension consists of simultaneously maintaining different "centroid" representations for each class, in particular "cross-media" centroids that correspond to pairs of modes. To classify new data points, different scores are derived from similarity measures between the new data point and these different centroids; a global classification score is finally obtained by suitably aggregating the individual scores. This method outperforms the multi-view logistic regression approach (using either the early fusion or the late fusion strategies) on a social media corpus - namely the ENRON email collection - on two very different categorization tasks (folder classification and recipient prediction).