On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Properties of measures of information in evidence and possibility theories
Fuzzy Sets and Systems
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Proceedings of the 18th international conference on World wide web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Combining multi-resolution evidence for georeferencing Flickr images
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Research and applications on georeferenced multimedia: a survey
Multimedia Tools and Applications
Web Semantics: Science, Services and Agents on the World Wide Web
Predicting User-to-content Links in Flickr Groups
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Georeferencing Flickr resources based on textual meta-data
Information Sciences: an International Journal
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We explore the task of automatically assigning geographic coordinates to photos on Flickr. Using an approach based on k-medoids clustering and Naive Bayes classification, we demonstrate that the task is feasible, although high accuracy can only be expected for a portion of all photos. Based on this observation, we stress the importance of adaptive approaches that estimate locations at different granularities for different photos.