Original Contribution: Stacked generalization
Neural Networks
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Hierarchical Model for Clustering and Categorising Documents
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Boosting image retrieval through aggregating search results based on visual annotations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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We explore the situation in which documents have to be categorized into more than one category system, a situation we refer to as multiple-view categorization. More particularly, we address the case where two different categorizers have already been built based on non-necessarily identical training sets, each one labeled using one category system. On the top of these categorizers considered as black-boxes, we propose some algorithms able to exploit a third training set containing a few examples annotated in both category systems. Such a situation arises for example in large companies where incoming mails have to be routed to several departments, each one relying on its own category system. We focus here on exploiting possible dependencies between category systems in order to refine the categorization decisions made by categorizers trained independently on different category systems. After a description of the multiple categorization problem, we present several possible solutions, based either on a categorization or reweighting approach, and compare them on real data. Lastly, we show how the multimedia categorization problem can be cast as a multiple categorization problem and assess our methods in this framework.