Learning similarity functions for event identification using support vector machines

KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Volumn , Issue , 2011, Pages 208-215

  Reuter, Timo ^a   Cimiano, Philipp ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Clustering classification; Data mining; Event identification; Machine learning; Similarity function; Support vector machine; Weight adjustment

Indexed keywords

CLUSTERING/CLASSIFICATION; DIFFERENT SIZES; EVENT IDENTIFICATION; GOLD STANDARDS; LAST.FM; LEARNING SIMILARITY; MACHINE LEARNING TECHNIQUES; OVERFITTING; SIMILARITY FUNCTIONS; SIMILARITY MEASURE; SOCIAL MEDIA; TRAINING DATA; WEIGHT ADJUSTMENT;

CLUSTERING ALGORITHMS; DATA MINING; INFORMATION RETRIEVAL; LEARNING SYSTEMS;

SUPPORT VECTOR MACHINES;

EID: 84862232482     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Allan, J., Papka, R., and Lavrenko, V. (1998). On-line new event detection and tracking. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 37-45. ACM.
2. Basu, S., Bilenko, M., and Mooney, R. (2003). Comparing and unifying search-based and similarity-based approaches to semi-supervised clustering. In Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, pages 42-49. Citeseer.
3. Becker, H., Naaman, M., and Gravano, L. (2010). Learning similarity metrics for event identification in social media. In Proceedings of the third ACM International Conference on Web search and Data Mining, pages 291-300.
4. Chang, C. and Lin, C. (2001). LIBSVM: a library for support vector machines.
5. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273-297.
6. Demiriz, A., Bennett, K., and Embrechts, M. (1999). Semisupervised clustering using genetic algorithms. Artificial neural networks in engineering (ANNIE-99), pages 809-814.
7. Eick, C., Rouhana, A., Bagherjeiran, A., and Vilalta, R. (2005). Using clustering to learn distance functions for supervised similarity assessment. Machine Learning and Data Mining in Pattern Recognition, pages 120-131.
8. Fakeri-Tabrizi, A., Tollari, S., Usunier, N., and Gallinari, P. (2011). Improving image annotation in imbalanced classification problems with ranking SVM. Multilingual Information Access Evaluation II. Multimedia Experiments, pages 291-294.
9. Finley, T. and Joachims, T. (2005). Supervised clustering with support vector machines. In Proceedings of the 22nd international conference on Machine learning, pages 217-224. ACM.
10. Firan, C., Georgescu, M., Nejdl, W., and Paiu, R. (2010). Bringing order to your photos: event-driven classification of flickr images based on social knowledge. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 189-198. ACM.
11. Freund, Y., Iyer, R., Schapire, R., and Singer, Y. (2003). An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 4:933-969.
12. Gao, J., Qi, H., Xia, X., and Nie, J. (2005). Linear discriminant model for information retrieval. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 290-297. ACM.
13. Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133-142. ACM.
14. Rattenbury, T. and Naaman, M. (2009). Methods for extracting place semantics from Flickr tags. ACM Transactions on the Web (TWEB), 3(1):1.
15. Rendle, S. and Schmidt-Thieme, L. (2006). Object identification with constraints.
16. Reuter, T., Cimiano, P., Drumond, L., Buza, K., and Schmidt-Thieme, L. (2011). Scalable event-based clustering of social media via record linkage techniques. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.