Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Probability-based fusion of information retrieval result sets
Artificial Intelligence Review
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Introduction to Information Retrieval
Introduction to Information Retrieval
A Probabilistic Model for User Relevance Feedback on Image Retrieval
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Generative model-based metasearch for data fusion in information retrieval
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Overview of the CLEF 2009 medical image retrieval track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
FIRE in ImageCLEF 2005: combining content-based image retrieval with textual information retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
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Nowadays, one of the main problems in information retrieval is filtering the great amount of information currently available. Late fusion techniques merge the outcomes of different information retrieval systems to generate a single result that, hopefully, could increase the overall performance by taking advantage of the strengths of all the individual systems. These techniques have a great flexibility and allow an efficient development of multimedia retrieval systems. The growing interest on these technologies has led to the creation of a subtrack in the ImageCLEF entirely devoted to them: the information fusion task. In this work, Intelligent Systems and Data Mining group approach to that task is presented. We propose the use of an evolutive algorithm to estimate the parameters of three of all the fusion approaches present in the literature.