Selected Publications of Tapio Elomaa

Edited Volumes

Journal of Intelligent Information Systems 37, 3 (2011). Special Issue on Data Analysis Methodologies for Intelligent Systems. With J. Rauch and P. Berka. Guest Editor's Introduction

Discovery Science, Proc. 14th Intl. Conf., DS 2011. Lecture Notes in Artificial Intelligence 6926 Springer, 2011. With J. Hollmén and H. Mannila.

Fundamenta Informaticae 99, 2 (2010). Special Issue on Methodologies for Intelligent Systems. With J. Rauch, Z. Ras, and P. Berka. Guest Editor's Introduction

Algorithms and Applications Essays Dedicated to Esko Ukkonen on the Occasion of His 60th Birthday. Lecture Notes in Computer Science 6060 Springer, 2010. With H. Mannila and P. Orponen.

Foundations of Intelligent Systems: Proc. 18th Intl. Symp., ISMIS 2009. Lecture Notes in Artificial Intelligence 5722 Springer, 2009. With J. Rauch, Z. Ras, and P. Berka.

Principles of Data Mining and Knowledge Discovery, Proc. 6th European Conference, PKDD 2002. Lecture Notes in Artificial Intelligence 2431. Springer, 2002. With H. Mannila and H. Toivonen.

Machine Learning: ECML 2002, Proc. 13th European Conference on Machine Learning. Lecture Notes in Artificial Intelligence 2430. Springer, 2002. With H. Mannila and H. Toivonen.

International Journals and Chapters in Books

• Multi-target regression with rule ensembles. Journal of Machine Learning Research 13 (Aug. 2012) 2367-2407. With T. Aho, B. Zenko, and S. Dzeroski.
• Online ChiMerge algorithm. In D.E. Holmes and L.C. Jain (eds.), Data Mining: Foundations and Intelligent Paradigms. Volume 2: Statistical, Bayesian, Time Series and Other Theoretical Aspects (pp. 199-216). Intelligent Systems Reference Library 24, Chapter 10. Springer, 2012. With P. Lehtinen and M. Saarela.
• An analysis of relevance vector machine regression. In J. Koronacki, Z.W. Ras, S.T. Wirzchon, and J. Kacprzyk (eds.), Advances in Machine Learning I. (Dedicated to the memory of Professor Ryszard S. Michalski.) (pp. 227-246). Studies in Computational Intelligence 262. Springer, 2010. With M. Saarela and K. Ruohonen.
• The cost of offline binary search tree algorithms and the complexity of the request sequence. Theoretical Computer Science 393, 1-3 (Mar. 2008) 231-239. With J. Kujala.
• On look-ahead and pathology in decision tree learning. Journal of Experimental and Theoretical Artificial Intelligence 17, 1-2 (Jan. 2005) 19-33. With T. Malinen.
• Selective Rademacher penalization and reduced error pruning of decision trees. Journal of Machine Learning Research 5 (Sept. 2004) 1107-1126. With M. Kääriäinen and T. Malinen.
• The difficulty of reduced error pruning of leveled branching programs. Annals of Mathematics and Artificial Intelligence 41, 1 (May 2004) 111-124. With M. Kääriäinen.
• Efficient multisplitting revisited: Optima-preserving elimination of partition candidates. Data Mining and Knowledge Discovery 8, 2 (Mar. 2004) 97-126. With J. Rousu.
• Flexible view recognition for indoor navigation based on Gabor filters and support vector machines. Pattern Recognition 36, 12 (Dec. 2003) 2769-2779. With I. Autio.
• Reduced Error Pruning of branching programs cannot be approximated to within a logarithmic factor. Information Processing Letters 87, 2 (July 31, 2003) 73-78. With R. Nock and M. Kääriäinen.
• Necessary and sufficient pre-processing in numerical range discretization. Knowledge and Information Systems 5, 2 (Apr. 2003) 162-182. With J. Rousu.
• Linear-time preprocessing in optimal numerical range partitioning. Journal of Intelligent Information Systems 18, 1 (Jan. 2002) 55-70. With J. Rousu.
• On the computational complexity of optimal multisplitting. Fundamenta Informaticae 47, 1-2 (Aug./Sept. 2001) 35-52. With J. Rousu.
• An analysis of reduced error pruning. Journal of Artificial Intelligence Research 15 (Sept. 2001) 163-187. With M. Kääriäinen.
• General and efficient multisplitting of numerical attributes. Machine Learning 36, 3 (Sept. 1999) 201-244. With J. Rousu.

Magazines and National Journals

• Applications of evaluation function convexity in data mining algorithms. Arpakannus (Special Issue on Data Mining and Artificial Intelligence) 1/2000 4-12. With J. Rousu.
• A review of How the Mind Works. AI Magazine 19, 3 (Fall 1998) 135-137.

International Conferences

• A walk from 2-norm SVM to 1-norm SVM. In Proc. 9th IEEE Intl. Conf. on Data Mining, ICDM 2009 (pp. 836-841). IEEE CS Press, 2009. With T. Aho and J. Kujala.
• Unsupervised classifier selection based on two-sample test. In J.-F. Boulicaut, M. R. Berthold, and T. Horváth (eds.), Discovery Science, Proc. 11th Intl. Conf., DS-2008 (pp. 28-39). Lecture Notes in Artificial Intelligence 5255. Springer, 2008. With T. Aho and J. Kujala.
• Ranking the uniformity of interval pairs. In W. Daelemans, B. Goethals, and K. Morik (eds), Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008, Part I (pp. 640-655). Lecture Notes in Artificial Intelligence 5211. Springer, 2008. With J. Kujala.
• Reducing splaying by taking advantage of working sets. In C. C. McGeoch (ed.), Experimental Algorithms, Proc. 7th Intl. Workshop, WEA 2008 (pp. 1-13). Lecture Notes in Computer Science 5038. Springer, 2008. With T. Aho and J. Kujala.
• Maintaining optimal multi-way splits for numerical attributes in data streams. In T. Washio, E. Suzuki, K. M. Ting, and A. Inokuchi (eds.), Advances in Knowledge Discovery and Data Mining, Proc. 12th Pacific-Asia Conf., PAKDD 2008 (pp. 544-553). Lecture Notes in Artificial Intelligence 5012. Springer, 2008. With P. Lehtinen.
• Obtaining low-arity discretizations from online data streams. In A. An, S. Matwin, Z. W. Ras, and D. Slezak (eds.), Foundations of Intelligent Systems, Proc. 17th Intl. Symp., ISMIS 2008 (pp. 90-99). Lecture Notes in Artificial Intelligence 4994. Springer, 2008. With P. Lehtinen and M. Saarela.
• Following the perturbed leader to gamble at multi-armed bandits. In M. Hutter, R. Servedio, and E. Takimoto (eds.), Algorithmic Learning Theory, Proc. 18th Intl. Conf., ALT '07, (pp. 166-180). Lecture Notes in Artificial Intelligence 4754. Springer, 2007. With J. Kujala.
• Improved algorithms for univariate discretization of continuous features. In J. Kok et al. (eds.), Knowledge Discovery in Databases: PKDD 2007, Proc. 11th European Conf. (pp. 188-199). Lecture Notes in Artificial Intelligence 4702. Springer, 2007. With J. Kujala.
• Poketree: a dynamically competitive data structure with good worst-case performance. In T. Asano (ed.), Algorithms and Computation, Proc. 17th Intl. Symp. ISAAC 2006 (pp. 277-288). Lecture Notes in Computer Science 4288. Springer, 2006. With J. Kujala.
• A Voronoi diagram approach to autonomous clustering. In L. Todorovski, N. Lavrac & K. P. Jantke (eds.), Discovery Science, Proc. 9th Intl. Conf., DS-2006 (pp. 137-148). Lecture Notes in Artificial Intelligence 4265. Springer, 2006. With H. Koivistoinen and M. Ruuska.
• Practical approximation of optimal multivariate discretization. In F. Esposito, Z. W. Ras, D. Malerba & G. Semeraro (eds.), Foundations of Intelligent Systems, Proc. 16th Intl. Symp., ISMIS '06 (pp. 612-621). Lecture Notes in Artificial Intelligence 4203. Springer, 2006. With J. Kujala and J. Rousu.
• On following the perturbed leader in the bandit setting. In S. Jain, H. U. Simon & E. Tomita (eds.), Algorithmic Learning Theory, Proc. 16th Intl. Conf., ALT '05 (pp. 371-385). Lecture Notes in Artificial Intelligence 3734. Springer, 2005. With J. Kujala.
• Approximation algorithms for minimizing empirical error by axis-parallel hyperplanes. In J. Gama, R. Camacho, P. Brazdil, A. Jorge & L. Torgo (eds.), Machine Learning: ECML 2005, Proc. 16th European Conf. (pp. 547-555). Lecture Notes in Artificial Intelligence 3720. Springer, 2005. With J. Kujala and J. Rousu.
• On autonomous k-means clustering.In M. S. Hacid, N. Murray, Z. W. Ras & S. Tsumoto (eds.), Foundations of Intelligent Systems, Proc. 15th Intl. Symp., ISMIS '05 (pp. 228-236). Lecture Notes in Artificial Intelligence 3488. Springer, 2005. With H. Koivistoinen.
• On lookahead heuristics in decision tree learning. In N. Zhong, Z. W. Ras, S. Tsumoto & E. Suzuki (eds.), Foundations of Intelligent Systems, Proc. 14th Intl. Symp., ISMIS '03 (pp. 445-453). Lecture Notes in Artificial Intelligence 2871. Springer, 2003. With T. Malinen.
• Rademacher penalization over decision tree prunings. In N. Lavrac, D. Gamberger,H. Blockeel & L. Todorovski (eds.), Machine Learning: ECML 2003, Proc. 14th European Conf. (pp. 193-204). Lecture Notes in Artificial Intelligence 2837. Springer, 2003. With M. Kääriäinen.
• On decision boundaries of Naïve Bayes in continuous domains. In N. Lavrac, D. Gamberger, L. Todorovski & H. Blockeel (eds.), Knowledge Discovery in Databases: PKDD 2003, Proc. 7th European Conf. (pp. 144-155). Lecture Notes in Artificial Intelligence 2838. Springer, 2003. With J. Rousu.
• Experiments with projection learning. In S. Lange, K. Satoh & C. H. Smith (eds.), Discovery Science, Proc. 5th Intl. Conf., DS '02 (pp. 127-140). Lecture Notes in Computer Science 2534. Springer, 2002. With J. T. Lindgren.
• Progressive Rademacher sampling. Proc. 18th Natl. Conf. on Artificial Intelligence, AAAI-2002 (pp. 140-145). AAAI Press & MIT Press, 2002. With M. Kääriäinen.
• Fast minimum error discretization. In C. Sammut & A. Hoffmann (eds.), Proc. 19th Intl. Conf. on Machine Learning, ICML'02 (pp. 131-138). Morgan Kaufmann, 2002. With J. Rousu.
• Partition-refining algorithms for learning finite state automata. In M.-S. Hacid, Z. W. Ras, D. A. Zighed & Y. Kodratoff (eds.), Foundations of Intelligent Systems, Proc. 13th ISMIS (pp. 232-243). Lecture Notes in Artificial Intelligence 2366. Springer, 2002.
• The difficulty of reduced error pruning of leveled branching programs. AI&M 7-2002, 7th Intl. Symp. on Artificial Intelligence and Mathematics. With M. Kääriäinen.
• Preprocessing opportunities in optimal numerical range partitioning. In Proc. 1st IEEE Intl. Conf. on Data Mining, ICDM '01 (pp. 115-122). IEEE Computer Society Press, 2001. With J. Rousu.
• On the practice of branching program boosting. In L. De Raedt & P. Flach (eds.), Machine Learning: ECML 2001, Proc. 12th European Conf. (pp. 133-144). Lecture Notes in Artificial Intelligence 2167. Springer, 2001. With M. Kääriäinen.
• On the complexity of optimal multisplitting. In Z. W. Ras & S. Ohsuga (eds.), Foundations of Intelligent Systems, Proc. 12th ISMIS (pp. 552-561). Lecture Notes in Artificial Intelligence 1932. Springer, 2000. With J. Rousu.
• Generalizing boundary points. Proc. 17th Natl. Conf. on Artificial Intelligence, AAAI-2000 (pp. 570-576). AAAI Press & MIT Press, 2000. With J. Rousu.
• Speeding up the search for optimal partitions. In J. Zytkow & J. Rauch (eds.), Principles of Data Mining and Knowledge Discovery, Proc. 3rd PKDD (pp. 89-97). Lecture Notes in Artificial Intelligence 1704. Springer, 1999.With J. Rousu.
• The biases of decision tree pruning strategies. In D. Hand, J. Kok & M. Berthold (eds.), Advances in Intelligent Data Analysis, Proc. 3rd IDA (pp. 63-74). Lecture Notes in Computer Science 1642. Springer, 1999.
• Predicting the speed of beer fermentation in laboratory and industrial scale. In J. Mira & J. Sanchez-Andrez (eds.), Engineering Applications of Bio-Inspired Artificial Neural Networks, Proc. 5th IWANN (pp. 893-901). Lecture Notes in Computer Science 1607. Springer, 1999. With J. Rousu and R. Aarts.
• Postponing the evaluation of attributes with a high number of boundary points. In M. Quafafou & J. Zytkow (eds.), Principles of Data Mining and Knowledge Discovery, Proc. 2nd PKDD (pp. 121-129). Lecture Notes in Artificial Intelligence 1510. Springer, 1998. With J. Rousu.
• Well-behaved attribute evaluation functions for numerical attributes. In Z. Ras & A. Skowron (eds.), Foundations of Intelligent Systems, Proc. 10th ISMIS (pp. 147-156). Lecture Notes in Artificial Intelligence 1325. Springer, 1997. With J. Rousu.
• Efficient multisplitting on numerical data. In J. Komorowski & J. Zytkow (eds.), Principles of Data Mining and Knowledge Discovery, Proc. 1st PKDD (pp. 178-188). Lecture Notes in Artificial Intelligence 1263. Springer, 1997. With J. Rousu.
• In defense of C4.5: notes on learning one-level decision trees. In W. Cohen & H. Hirsh (eds.), Machine Learning: Proc. 11th Intl. Conf. (pp. 62-69). Morgan Kaufmann, 1994.
• Learning decision trees for mapping the local environment in mobile robot navigation. In Proc. MLC-COLT Workshop on Robot Learning (pp. 119-125). New Brunswick NJ, 1994. With I. Sillitoe.
• A geometric approach to feature selection. In F. Bergadano & L. De Raedt (eds.), Machine Learning: ECML-94, Proc. 7th European Conf. (pp. 351-354). Lecture Notes in Artificial Intelligence 784. Springer, 1994. With E. Ukkonen.

Technical Reports

• Learning multi-target regression with rule ensembles. Report 18. Department of Software Systems, Tampere University of Technology, Nov. 2011. 47 pp. With T. Aho, B. Zenko, and S. Dzeroski.
• On the splitting properties of common attribute evaluation functions. Report C-2000-1, Dept. of Computer Science, Univ. of Helsinki. With J. Rousu.
• Uses of convexity in numerical domain partitioning. NeuroCOLT2 Technical Report NC2-TR-2000-074. With J. Rousu.
• Proceedings of the 1999 HeCSE Winter School. Report C-1999-1, Dept. of Computer Science, Univ. of Helsinki.
• Learning decision trees from noisy examples. Report A-1991-3. Dept. of Computer Science, Univ. of Helsinki. With J. Kivinen.

Dissertation

• Tools and techniques for decision tree learning (also in here). Ph.D. Thesis, Report A-1996-2. Dept. of Computer Science, Univ. of Helsinki.

In Finnish

• Oppimisen teoriasta ja teorian oppimisesta (virkaanastujaisesitelmä). Tietojenkäsittelytiede 23 (kesäkuu 2005) 17-25.
• Darwin ja tekoäly. Kanava 27, 1 (Jan. 1999), 34-37.
• Puheenvuoro: "Imurirobotti viipyy vielä". Tieto & kone -sivut, Helsingin Sanomat, 13. joulukuuta 1996, s. D2.
• Tekoäly ei yllä ihmisen verroille. Helsingin Sanomat, 20. maaliskuuta 1996, s. A2.