Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank for information retrieval (LR4IR 2007)
ACM SIGIR Forum
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
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Most multimedia retrieval problem can be described by ranking model, i.e. the images in the database could be ranked according to the similarity compared with the query image. Existing ranking models generally use the features that are pre-defined by experts. This paper utilized machine learning techniques to automatically select useful features for ranking. We first generate a set of feature subsets by putting each feature into an individual feature subset. Then we sort these feature subsets according to the ranking performances. Third, two neighbor feature subsets in the ranked order are pairwised to generate a new feature subset. The new feature subsets are sorted based on the new ranking performance. Iterate until reach the pre-defined stop point. Experimental results on .gov dataset and Caltech101 development set show the effectiveness and efficiency of the proposed algorithm.