Hierarchical mixtures of experts and the EM algorithm
Neural Computation
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Linear discriminant model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
TRECVID: benchmarking the effectiveness of information retrieval tasks on digital video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Probabilistic latent query analysis for combining multiple retrieval sources
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Extreme video retrieval: joint maximization of human and computer performance
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Merging storyboard strategies and automatic retrieval for improving interactive video search
Proceedings of the 6th ACM international conference on Image and video retrieval
Establishing the utility of non-text search for news video retrieval with real world users
Proceedings of the 15th international conference on Multimedia
Graph-Based Pairwise Learning to Rank for Video Search
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Relationship identification for social network discovery
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Machine Learning
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Learning a good ranking function plays a key role for many applications including the task of (multimedia) information retrieval. While there are a few rank learning methods available, most of them need to explicitly model the relations between every pair of relevant and irrelevant documents, and thus result in an expensive training process for large collections. The goal of this paper is to propose a general rank learning framework based on the margin-based risk minimization principle and develop a set of efficient rank learning approaches that can model the ranking relations with much less training time. Its flexibility allows a number of margin-based classifiers to be extended to their rank learning counterparts such as the ranking logistic regression developed in this paper. Experimental results show that this efficient learning algorithm can successfully learn a highly effective retrieval function for multimedia retrieval on the TRECVID'03-'05 collections.