[{"publisher":"ML Research Press","year":"2013","issue":"3","related_material":{"record":[{"status":"public","id":"1794","relation":"later_version"}]},"publication_status":"published","external_id":{"isi":["000381149500002"]},"main_file_link":[{"url":"http://proceedings.mlr.press/v28/takhanov13.pdf?CFID=105472548&CFTOKEN=5c5859b5d97b4439-27B4AC58-BA92-A964-B598CAACEE6CC515","open_access":"1"}],"publist_id":"4672","quality_controlled":"1","abstract":[{"lang":"eng","text":"We consider Conditional Random Fields (CRFs) with pattern-based potentials defined on a chain. In this model the energy of a string (labeling) x1...xn is the sum of terms over intervals [i,j] where each term is non-zero only if the substring xi...xj equals a prespecified pattern α. Such CRFs can be naturally applied to many sequence tagging problems.\r\nWe present efficient algorithms for the three standard inference tasks in a CRF, namely computing (i) the partition function, (ii) marginals, and (iii) computing the MAP. Their complexities are respectively O(nL), O(nLℓmax) and O(nLmin{|D|,log(ℓmax+1)}) where L is the combined length of input patterns, ℓmax is the maximum length of a pattern, and D is the input alphabet. This improves on the previous algorithms of (Ye et al., 2009) whose complexities are respectively O(nL|D|), O(n|Γ|L2ℓ2max) and O(nL|D|), where |Γ| is the number of input patterns.\r\nIn addition, we give an efficient algorithm for sampling. Finally, we consider the case of non-positive weights. (Komodakis &amp; Paragios, 2009) gave an O(nL) algorithm for computing the MAP. We present a modification that has the same worst-case complexity but can beat it in the best case. "}],"language":[{"iso":"eng"}],"publication":"ICML'13 Proceedings of the 30th International Conference on International","intvolume":"        28","volume":28,"status":"public","conference":{"location":"Atlanta, GA, USA","end_date":"2013-06-21","start_date":"2013-06-16","name":"ICML: International Conference on Machine Learning"},"corr_author":"1","date_updated":"2025-09-29T14:28:48Z","oa":1,"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","citation":{"ama":"Takhanov R, Kolmogorov V. Inference algorithms for pattern-based CRFs on sequence data. In: <i>ICML’13 Proceedings of the 30th International Conference on International</i>. Vol 28. ML Research Press; 2013:145-153.","ista":"Takhanov R, Kolmogorov V. 2013. Inference algorithms for pattern-based CRFs on sequence data. ICML’13 Proceedings of the 30th International Conference on International. ICML: International Conference on Machine Learning, JMLR, vol. 28, 145–153.","ieee":"R. Takhanov and V. Kolmogorov, “Inference algorithms for pattern-based CRFs on sequence data,” in <i>ICML’13 Proceedings of the 30th International Conference on International</i>, Atlanta, GA, USA, 2013, vol. 28, no. 3, pp. 145–153.","apa":"Takhanov, R., &#38; Kolmogorov, V. (2013). Inference algorithms for pattern-based CRFs on sequence data. In <i>ICML’13 Proceedings of the 30th International Conference on International</i> (Vol. 28, pp. 145–153). Atlanta, GA, USA: ML Research Press.","chicago":"Takhanov, Rustem, and Vladimir Kolmogorov. “Inference Algorithms for Pattern-Based CRFs on Sequence Data.” In <i>ICML’13 Proceedings of the 30th International Conference on International</i>, 28:145–53. ML Research Press, 2013.","short":"R. Takhanov, V. Kolmogorov, in:, ICML’13 Proceedings of the 30th International Conference on International, ML Research Press, 2013, pp. 145–153.","mla":"Takhanov, Rustem, and Vladimir Kolmogorov. “Inference Algorithms for Pattern-Based CRFs on Sequence Data.” <i>ICML’13 Proceedings of the 30th International Conference on International</i>, vol. 28, no. 3, ML Research Press, 2013, pp. 145–53."},"article_processing_charge":"No","date_created":"2018-12-11T11:56:41Z","department":[{"_id":"VlKo"}],"month":"06","author":[{"full_name":"Takhanov, Rustem","id":"2CCAC26C-F248-11E8-B48F-1D18A9856A87","last_name":"Takhanov","first_name":"Rustem"},{"id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","full_name":"Kolmogorov, Vladimir","last_name":"Kolmogorov","first_name":"Vladimir"}],"type":"conference","page":"145 - 153","alternative_title":["JMLR"],"day":"01","title":"Inference algorithms for pattern-based CRFs on sequence data","isi":1,"oa_version":"Submitted Version","_id":"2272","date_published":"2013-06-01T00:00:00Z","scopus_import":"1"}]