[1]	Tapio Pahikkala, Antti Airola, and Tapio Salakoski. Linear time feature selection for regularized least-squares, 2010. Submitted. [ bib | http ]
[2]	Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast evolutionary maximum margin clustering. In Léon Bottou and Michael Littman, editors, ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, volume 382 of ACM International Conference Proceeding Series, pages 361-368, New York, NY, USA, June 2009. ACM. [ bib | DOI ]
[3]	Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jouni Järvinen, and Jorma Boberg. An efficient algorithm for learning to rank from preference graphs. Machine Learning, 75(1):129-165, 2009. [ bib | DOI | .pdf ]
[4]	Tapio Pahikkala, Antti Airola, Jorma Boberg, and Tapio Salakoski. Exact and efficient leave-pair-out cross-validation for ranking RLS. In Timo Honkela, Matti Pöllä, Mari-Sanna Paukkeri, and Olli Simula, editors, Proceedings of the 2nd International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'08), pages 1-8. Helsinki University of Technology, 2008. [ bib | .pdf ]
[5]	Tapio Pahikkala, Antti Airola, Hanna Suominen, Jorma Boberg, and Tapio Salakoski. Efficient AUC maximization with regularized least-squares. In Anders Holst, Per Kreuger, and Peter Funk, editors, Proceedings of the 10th Scandinavian Conference on Artificial Intelligence (SCAI 2008), volume 173 of Frontiers in Artificial Intelligence and Applications, pages 12-19. IOS Press, Amsterdam, Netherlands, 2008. [ bib | .pdf ]
[6]	Evgeni Tsivtsivadze, Tapio Pahikkala, Antti Airola, Jorma Boberg, and Tapio Salakoski. A sparse regularized least-squares preference learning algorithm. In Anders Holst, Per Kreuger, and Peter Funk, editors, Proceedings of the 10th Scandinavian Conference on Artificial Intelligence (SCAI 2008), volume 173, pages 76-83. IOS Press, 2008. [ bib | .pdf ]
[7]	Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jorma Boberg, and Tapio Salakoski. Learning to rank with pairwise regularized least-squares. In Thorsten Joachims, Hang Li, Tie-Yan Liu, and ChengXiang Zhai, editors, SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, pages 27-33, 2007. [ bib | .pdf ]
[8]	Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski. Fast n-fold cross-validation for regularized least-squares. In Timo Honkela, Tapani Raiko, Jukka Kortela, and Harri Valpola, editors, Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence, pages 83-90, Espoo, Finland, 2006. Otamedia Oy. [ bib | .pdf ]