University of Helsinki Department of Mathematics and Statistics
Faculty of Science
Faculty of Social Sciences

 

Petri P. M. Koistinen: Publications

Scientific publications

  • B. Mathew, A. M. Bauer, P. Koistinen, T. C. Reetz, J. Léon, and M. J. Sillanpää. Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. Heredity, 109:235-245, 2012.

  • L. Holmström and P. Koistinen. Pattern recognition. Wiley Interdisciplinary Reviews: Computational Statistics, 2:404-413, 2010. doi:10.1002/wics.99.

  • P. Koistinen, L. Holmström, and E. Tomppo. Smoothing methodology for predicting regional averages in multi-source forest inventory. Remote Sensing of Environment, 112(3):862-871, 2008. doi:10.1016/j.rse.2007.06.019.

  • P. Koistinen, L. Holmström, and E. Tomppo. Using local linear smoothing for predicting regional averages in multi-source forest inventory. In C. Kleinn, J. Nieschulze, and B. Sloboda, editors, Remote Sensing and Geographical Information Systems for Environmental Studies: Applications in Forestry, Schriften aus der Forstlichen Fakultät der Universität Göttingen und der Niedersächsischen Forstlichen Versuchsanstalt, Band 138, pages 275-283, 2005.

  • V. Kolehmainen, S. Siltanen, S. Järvenpää, J. P. Kaipio, P. Koistinen, M. Lassas, J. Pirttilä, and E. Somersalo. Statistical inversion for medical X-ray tomography with few radiographs II: Application to dental radiology. Physics in Medicine and Biology, 48(10):1465-1490, 2003.

  • S. Siltanen, V. Kolehmainen, S. Järvenpää, J. P. Kaipio, P. Koistinen, M. Lassas, J. Pirttilä, and E. Somersalo. Statistical inversion for medical X-ray tomography with few radiographs I: General theory. Physics in Medicine and Biology, 48(10):1437-1463, 2003.

  • L. Holmström, P. Koistinen, J. Sarvas, E. Tomppo, and L. Zurk. A polarimetric scattering model and a new approach to the estimation of forest parameters. In Jouni Jussila, Tuomo Nygrén, and Väinö Kelhä, editors, The IX Meeting of Finnish National COSPAR and ANTARES Fall Seminar 2002, page 38, Oulu, Finland, 2002.

  • Lisa M. Zurk, Petri Koistinen, Jukka Sarvas, and Lasse Holmström. Electromagnetic scattering model for forest remote sensing. Research Reports A38, Rolf Nevanlinna Institute, 2002. (PostScript, 38 pages, 2039926 bytes) (PDF, 310774 bytes)

  • L. Holmström, P. Erästö, P. Koistinen, J. Weckström, and A. Korhola. Using smoothing to reconstruct the Holocene temperature in Lapland. In E. J. Wegman and Y. Martinez, editors, Computing Science and Statistics, Volume 32; Modeling the Earth's Systems: Physical to Infrastructural. Proceedings of the 32nd Symposium on the interface of computing science and statistics, New Orleans, Lousiana, April 5-8, pages 425-438. Interface Foundation of North America, 2000.

  • L. Holmström, P. Koistinen, F. Hoti, and P. Erästö. Classification of complex data. In Year 2000, 5th World Congress of the Bernoulli Society for Mathematical Statistics and Probability and 63rd Meeting of the Institute of Mathematical Statistics. Program, Abstracts and Directory of Participants, page 76, Guanajuato, Mexico, 2000. Invited paper.

  • L. Holmström, F. Hoti, and P. Koistinen. Experiments in polychotomous classification. In Bulletin of the International Statistical Institute, ISI 99, the 52nd Session of the International Statistical Institute, August 10 -- 18, 1999, Helsinki, Finland, Contributed Papers, Tome LVIII, Three Books, Book 2, page 41, 1999.

  • P. Koistinen. Asymptotic theory for regularization: One-dimensional linear case. In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems 10, pages 294-300, 1998. (PostScript, 7 pages, 156692 bytes)

  • L. Holmström, P. Koistinen, J. Laaksonen, and E. Oja. Neural and statistical classifiers--taxonomy and two case studies. IEEE Transactions on Neural Networks, 8(1):5-17, January 1997. doi:10.1109/72.554187.

  • P. Koistinen. Convergence in noisy training. In J. C. Mason S. W. Ellacott and I. J. Anderson, editors, Mathematics of Neural Networks: Models, Algorithms and Applications, pages 220-224. Kluwer Academic Publishers, 1997.

  • P. Koistinen. Large sample results for training with noise. In Proc. Measurement '97, Smolenice Castle, Slovak Republic, May 29-31, pages 344-347, 1997.

  • L. Holmström, P. Koistinen, J. Laaksonen, and E. Oja. Comparison of neural and statistical classifiers--theory and practice. Research Reports A13, Rolf Nevanlinna Institute, 1996.

  • L. Holmström, P. Koistinen, J. Laaksonen, and E. Oja. Neural network and statistical perspectives of classification. In Proc. ICPR-96, Vienna, pages IV: 286-290, Los Alamitos, CA, 1996. IEEE Computer Society Press.

  • P. Koistinen. Convergence of minimization estimators trained under additive noise. Research Reports A12, Rolf Nevanlinna Institute, 1995. Doctoral thesis, Helsinki University of Technology. (PDF, 623409 bytes)

  • P. Koistinen. Unsupervised formation of feature detectors using residual inputs. In S. Gielen and B. Kappen, editors, ICANN'93: Proceedings of the International Conference on Artificial Neural Networks, pages 219-223. Springer-Verlag, 1993.

  • L. Holmström and P. Koistinen. Using additive noise in back-propagation training. IEEE Transactions on Neural Networks, 3(1):24-38, January 1992. doi:10.1109/72.105415.

  • P. Koistinen and L. Holmström. Kernel regression and backpropagation training with noise. In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors, Advances in Neural Information Processing Systems 4, pages 1033-1039, San Mateo, CA, 1992. Morgan Kaufmann Publishers.

Lecture notes

  • P. Koistinen. Lineaaristen mallien kurssi. Luentomoniste, 2005. (PDF, 358597 bytes)

  • P. Koistinen. Tilastollinen hahmontunnistus. Luentomoniste, 2002. (PDF, 625686 bytes)

  • P. Koistinen and L. Holmström. Hahmontunnistus. In K. Auranen, J. Lukkarinen, J. Seppänen, and S. Vänska, editors, Sovelletun matematiikan harjoitustyökokoelma, Rolf Nevanlinna -instituutti, C36, pages 65-84, 2001.