CoSCo Publications

The publications of the Adaptive Computing group can be found at http:///www.hiit.fi/adaptive-computing.

1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017

2017

2016

2015

2014

  • Sotiris Tasoulis, Lu Cheng, Niko Välimäki, Nicholas Croucher, Simon Harris, William Hanage, Teemu Roos, and Jukka Corander, “Random Projection Based Clustering for Population Genomics”, IEEE Big Data 2014: IEEE International Conference on Big Data 2014.

  • Wolfgang Dvořák, Matti Järvisalo, Johannes Peter Wallner, and Stefan Woltran. Complexity-Sensitive Decision Procedures for Abstract Argumentation. Artificial Intelligence 206:53-78, 2014.

  • Jeremias Berg and Matti Järvisalo. SAT-Based Approaches to Treewidth Computation: An Evaluation. In Proceedings of the 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI 2014), pages 328-335. IEEE Computer Society, 2014.

  • Emilia Oikarinen and Matti Järvisalo. Answer Set Solver Backdoors. In Eduardo Ferme and Joao Leite, editors, Proceedings of the 14th International Conference on Logics in Artificial Intelligence (JELIA 2014), volume 8761 of Lecture Notes in Computer Science, pages 674-683. Springer, 2014.

  • Athukorala, K., Oulasvirta, A., Glowacka, D., Vreeken, J. and G. Jacucci, “Interaction Model to Predict Subjective-Specificity of Search Results”, UMAP 2014: 22nd Conference on User Modeling, Adaptation and Personalization.

  • Glowacka, D. and S. Hore, “Balancing Exploration – Exploitation in Image Retrieval”, UMAP 2014: 22nd Conference on User Modeling, Adaptation and Personalization.

  • Ruotsalo, T., Peltonen, J., Eugster, M.J.A., Glowacka, D., Reijonen, A., Jacucci, G., Myllymaki, P. and S. Kaski, “IntentRadar: Search User Interface that Anticipates Users Search Intents”, CHI Conference on Human Factors in Computing Systems (extended abstracts).

  • Melih Kandemir, Akos Vetek, Mehmet Gönen, Arto Klami and Samuel Kaski.  Multi-task and multi-view learning of user state. Neurocomputing 139(2):97-106, 2014.

  • Antti Hyttinen, Frederick Eberhardt, and Matti Järvisalo. Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. In Jin Tian and Nevin L. Zhang, editors, Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), pages 340-349. AUAI Press, 2014.

  • Xiannian Fan, Brandon Malone, and Changhe Yuan. Finding Optimal Bayesian Network Structures with Constraints Learned from Data. In Jin Tian and Nevin L. Zhang, editors, Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), pages 200-209. AUAI Press, 2014.

  • Xiannian Fan, Changhe Yuan, and Brandon Malone. Tightening Bounds for Bayesian Network Structure Learning. In Carla E. Brodley and Peter Stone, editors, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), pages 2439-2445. AAAI Press, 2014.

  • Jeremias Berg, Matti Järvisalo, and Brandon Malone. Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability. In Jukka Corander and Samuel Kaski, editors, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014), pages 86-95, 2014.

  • R. Eggeling, T. Roos, P. Myllymäki, and I. Grosse. Robust learning of inhomogeneous PMMs. In Proc. 17th International Conference on Artificial Intelligence and Statistics (AISTATS-2014), 2014.

  • A. Barron, T. Roos, and K. Watanabe. Bayesian properties of normalized maximum likelihood and its fast computation, to appear in Proc. IEEE International Symposium on Information Theory (ISIT-2014), IEEE Press, 2014.

  • M. Sherman, G. Clark, Y. Yang, S. Sugrim, A. Modig, J. Lindqvist, A. Oulasvirta, and T. Roos. User-generated free-form gestures for authentication: security and memorability. In Proc. 12th International Conference on Mobile Systems, Applications, and Services (MobiSys-2014), ACM Press, 2014.

  • Kerstin Bunte, Matti Järvisalo, Jeremias Berg, Petri Myllymäki, Jaakko Peltonen, and Samuel Kaski. Optimal Neighborhood Preserving Visualization by Maximum Satisfiability. In Carla E. Brodley and Peter Stone, editors, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), pages 1694-1700. AAAI Press, 2014.

  • Brandon Malone, Juho-Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, and Petri Myllymäki. Predicting the Hardness of Learning Bayesian Networks. In Carla E. Brodley and Peter Stone, editors, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), pages 2460-2466. AAAI Press, 2014.

  • Matti Järvisalo and Janne H. Korhonen. Conditional Lower Bounds for Failed Literals and Related Techniques. In Uwe Egly and Carsten Sinz, editors, Proceedings of the 17th International Conference on Theory and Applications of Satisfiability Testing (SAT 2014), volume 8561 of Lecture Notes in Computer Science, pages 75-84. Springer, 2014.

  • Brandon M. Malone and Changhe Yuan. A Depth-First Branch and Bound Algorithm for Learning Optimal Bayesian Networks. In Madalina Croitoru, Sebastian Rudolph, Stefan Woltran, and Christophe Gonzales, editors, Revised Selected Papers of the Third International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR 2013), volume 8323 of Lecture Notes in Computer Science, pages 111-122. Springer, 2014.

  • Nitin Sukhija, Brandon Malone, Srishti Srivastava, Ioana Banicescu, and Florina Ciorba. Portfolio-based Selection of Robust Dynamic Loop Scheduling Algorithms using Machine Learning. In IEEE International Symposium on Parallel & Distributed Processing Workshops, pages 1638-1647. IEEE, 2014.

  • Anton Belov, Daniel Diepold, Marijn J.H. Heule, and Matti Järvisalo (editors). Proceedings of SAT Competition 2014: Solver and Benchmark Descriptions. Volume B-2014-2 of Department of Computer Science Series of Publications B, University of Helsinki, 2014. ISBN 978-951-51-0043-6.

  • Jussi Määttä, Samuli Siltanen, and Teemu Roos, (2014). A Fixed-Point Image Denoising Algorithm with Automatic Window Selection, in Proc. 5th European Workshop on Visual Information Processing (EUVIP 2014).

2013

2012

  • T. Roos, P. Myllymäki, and T. Jaakkola, Editorial: Special issue on the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010). International Journal of Approximate Reasoning 59 (2012) 9, 1303-1304.

  • H. Wettig, J. Nouri, K. Reshetnikov, and R. Yangarber, Information-theoretic Methods for Analysis and Inference in Etymology. Pp. 53-56 in Proceedings of the Fifth Workshop on Information-theoretic Methods in Science and Engineering.

  • H. Wettig, K. Reshetnikov, and R. Yangarber, Using Context and Phonetic Features in Models of Etymological Sound Change. Pp. 108-116 in Proceedings of the EACL 2012 Joint Workshop of LINGVIS.

  • Matti Järvisalo, Armin Biere, and Marijn Heule. Simulating Circuit-Level Simplifications on CNF. Journal of Automated Reasoning 49(4):583-619, 2012.

  • Matti Järvisalo, Arie Matsliah, Jakob Nordström, and Stanislav Živný. Relating Proof Complexity Measures and Practical Hardness of SAT. In Michela Milano, editor, Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming (CP 2012), volume 7514 of Lecture Notes in Computer Science (LNCS), pages 316-331. Springer, 2012.

  • Matti Järvisalo, Marijn Heule, and Armin Biere. Inprocessing Rules.
    In Bernhard Gramlich, Dale Miller, and Uli Sattler, editors, Proceedings of the 6th International Joint Conference on Automated Reasoning (IJCAR 2012), volume 7364 of Lecture Notes in Computer Science (LNCS/LNAI), pages 355-370. Springer, 2012.

  • Wolfgang Dvořák, Matti Järvisalo, Johannes Peter Wallner, and Stefan Woltran. Complexity-Sensitive Decision Procedures for Abstract Argumentation. In Thomas Eiter and Sheila McIlraith, editors, Proceedings of the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR 2012), pages 54-64. AAAI Press, 2012.

  • Matti Järvisalo, Petteri Kaski, Mikko Koivisto, and Janne H. Korhonen. Finding Efficient Circuits for Ensemble Computation. In Alessandro Cimatti and Roberto Sebastiani, editors, Proceedings of the 15th International Conference on Theory and Applications of Satisfiability Testing (SAT 2012), volume 7317 of Lecture Notes in Computer Science, pages 369-382. Springer, 2012.

  • Lauri Hella, Matti Järvisalo, Antti Kuusisto, Juhana Laurinharju, Tuomo Lempiäinen, Kerkko Luosto, Jukka Suomela, and Jonni Virtema. Weak Models of Distributed Computing, with Connections to Modal Logic. In Darek Kowalski and Alessandro Panconesi, editors, Proceedings of the 31st Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC 2012), pages 185-194. ACM Press, 2012.

  • Matti Järvisalo, Daniel Le Berre, Olivier Roussel, and Laurent Simon. The International SAT Solver Competitions. AI Magazine 33(1):89-92, 2012.

  • Adrian Balint, Anton Belov, Daniel Diepold, Simon Gerber, Matti Järvisalo, and Carsten Sinz (editors). Proceedings of SAT Challenge 2012: Solver and Benchmark Descriptions. Volume B-2012-2 of Department of Computer Science Series of Publications B, University of Helsinki, 2012. ISBN 978-952-10-8106-4.

  • Wolfgang Dvořák, Matti Järvisalo, Johannes Peter Wallner, and Stefan Woltran. CEGARTIX: A SAT-Based Argumentation System. In 3rd Workshop on Pragmatics of SAT (PoS 2012), 2012.

  • B. Malone and C. Yuan. A Bounded Error, Anytime Parallel Algorithm for Exact Bayesian Network Structure Learning. In Proceedings of the 6th European Workshop on Probabilistic Graphical Models (PGM-2012), Granada, Spain, September 2012.

  • A. Oulasvirta, A. Pihlajamaa, J. Perkiö, T. Vähakangas, D. Ray, N. Vainio, P. Myllymäki, T. Hasu, Long-term Effects of Ubiquitous Surveillance at Home. In Proceedings of the 14th International Conference on Ubiquitous Computing (Ubicomp), September, 2012.

  • S. de Rooij, W. Kotłowski, J. Rissanen, P. Myllymäki, T. Roos, and K. Yamanishi (editors), Proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE 2012).

  • C. Yuan and B. Malone, An Improved Admissible Heuristic for Finding Optimal Bayesian Networks. In the proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-2012), Catalina Island, California, August 2012.

  • C.D. Giurcaneanu, P. Luosto and P. Kontkanen, On The Performance Of Histogram-Based Entropy Estimators. In Proceedings of the 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP'2012), September 2012, Santander, Spain.

  • P. Luosto, C.D. Giurcaneanu and P. Kontkanen, Construction of irregular histograms by penalized maximum likelihood: a comparative study. IEEE Information Theory Workshop 2012 (ITW), Lausanne, Switzerland, 3-7 September 2012.

  • J. Rissanen, Optimal Estimation of Parameters. Cambridge University Press, 2012.

  • S. Bhattacharya, P. Floréen, A. Forsblom, S. Hemminki, P. Myllymäki, P. Nurmi, T. Pulkkinen, A. Salovaara, Ma$$iv€ - An Intelligent Mobile Grocery Assistant. In Proceedings of the 8th International Conference on Intelligent Environments (IE'12, June 2012, Guanajuato, Mexico).

2011

2010

  • P. Myllymäki, T. Roos and T. Jaakkola (eds.), Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010). HIIT Publications 2010-2.

  • K. Yamanishi, I. Kontoyiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus (eds.), Proceedings of the Third Workshop on Information Theoretic Methods in Science and Engineering. TICSP series 55, Tampere International Center for Signal Processing, 2010.

  • J. Perkiö, A. Tuominen, T. Vähäkangas and P. Myllymäki, Image Similarity: From Syntax to Weak Semantics. Multimedia Tools and Applications, DOI: 10.1007/s11042-010-0562-7, July 14, 2010.

  • T. Silander, T. Roos, and P. Myllymäki, Learning Locally Minimax Optimal Bayesian Networks, International Journal of Approximate Reasoning 51 (2010) 5 (June), 544-557.

  • P. Maksimainen, Finding groups in virtual communities. M.Sc. thesis, Report C-2010-28, Department of Computer Science, University of Helsinki, Finland.

  • J. Rissanen, T. Roos, and P. Myllymäki, Model Selection by Sequentially Normalized Least Squares, Journal of Multivariate Analysis 101 (2010) 4 (April), 839-849.

  • J. Rissanen, The MDL Principle, in C. Sammut and G.I. Webb (editors), Encyclopedia of Machine Learning, Springer, 2010.

  • T. Roos, Terveisiä huippuyliopistoista, Tietojenkäsittelytiede 30 (2010), 7-12.

  • D.F. Schmidt and T. Roos. On the consistency of sequentially normalized least squares, invited paper (extended abstract) in Proc. 3rd Workshop on Information Theoretic Methods in Science and Engineering (WITMSE-10), Tampere International Center for Signal Processing.

  • P.-H. Lai, T. Roos, and J. O'Sullivan, MDL Hierarchical Clustering for Stemmatology, in Proceedings of the 2010 IEEE International Symposium on Information Theory (ISIT-2010).

  • Y. Zou, (2010). Structural EM methods in phylogenetics and stemmatology, Master's Thesis, Department of Computer Science, University of Helsinki.

2009

  • J. Perkiö, T. Tuytelars and W. Buntine, Exploring Scale-Induced Feature Hierarchies in Natural Images. Pp. 25-31 in Proceedings of the Eight International Conference on Machine Learning and Applications, edited by M. A. Wani, M. Kantardzic, V. Palade, L. Kurgan and Y. Qi. IEEE Computer Society, 2009.

  • T. Mononen, Computing the Stochastic Complexity of Simple Probabilistic Graphical Models. Doctoral dissertation, University of Helsinki. Department of Computer Science, Series of Publications A, Report A-2009-10.

  • P. Kontkanen, Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering. Doctoral dissertation, University of Helsinki. Department of Computer Science, Series of Publications A, Report A-2009-11.

  • J. Perkiö, A. Tuominen and P. Myllymäki, Image Similarity: From Syntax to Weak Semantics using Multimodal Features with Application to Multimedia Retrieval. Pp. 213-219 in Proceedings of the 2009 International Conference on Multimedia Information Networking and Security. IEEE Computer Society, 2009.

  • J. Perkiö and P. Myllymäki, Magrathea: Building and Analyzing Ubiquitous and Social Systems. Pp. 66-75 in Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technologies, edited by P. Boldi, G. Vizzari, G. Pasi and R. Baeza-Yates. IEEE Computer Society, 2009.

  • J. Perkiö and A. Hyvärinen, Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval (winner of the best student paper award). Pages 704-714 in Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN-09), edited by C. Alippi, M. Polycarpou, C. Panayiotou and G. Ellinas. Lecture Notes in Computer Science 5769, Springer 2009.

  • T. Roos, P. Myllymäki, and J. Rissanen, MDL Denoising Revisited. IEEE Trans. Signal Processing 57 (2009) 9 (September), 3347-3360.

  • K. Kumpulainen, P. Myllymäki, R. Smeds, J. Kronqvist and P. Pöyry-Lassila, VISCI Virtual Intelligent Space for Collaborative Innovation - Project Description. Pp. 75-82 in Proceedings of the 1st International Symposium on Tangible Software Enginering Education (STANS09), edited by T. Nakamura, H. Kameda, S. Iwashita, A. Takashima and H. Maruyama.

  • J. Heikkonen, I. Kontoyiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus (eds.), Proceedings of the Second Workshop on Information Theoretic Methods in Science and Engineering. TICSP series 49, Tampere International Center for Signal Processing, 2009.

  • T. Merivuori and T. Roos, Some Observations on the Applicability of Normalized Compression Distance to Stemmatology. In Proceedings of the Second Workshop on Information Theoretic Methods in Science and Engineering, edited by J. Heikkonen, I. Kontoyiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus. TICSP series 49, Tampere International Center for Signal Processing, 2009.

  • P. Myllymäki, A framework for MDL clustering. In Proceedings of the Second Workshop on Information Theoretic Methods in Science and Engineering, edited by J. Heikkonen, I. Kontoyiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus. TICSP series 49, Tampere International Center for Signal Processing, 2009.

  • E. Elovaara and P. Myllymäki, MDL-based attribute models in naive Bayes classification. In Proceedings of the Second Workshop on Information Theoretic Methods in Science and Engineering, edited by J. Heikkonen, I. Kontoyiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus. TICSP series 49, Tampere International Center for Signal Processing, 2009.

  • T. Roos and B. Yu, Recovering Sparse Models by Parameter Transformations: Applications in Markov Models and Logistic Regression. In Proceedings of the Second Workshop on Information Theoretic Methods in Science and Engineering, edited by J. Heikkonen, I. Kontoyiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus. TICSP series 49, Tampere International Center for Signal Processing, 2009.

  • J. Rissanen, Model Selection and Testing by the MDL Principle. Chapter 2 in F. Emmert-Streib and M. Dehmer, eds., Information Theory and Statistical Learning, Springer, 2009.

  • T. Roos and T. Heikkilä, Evaluating Methods for Computer-Assisted Stemmatology using Artificial Benchmark Data Sets. Literary & Linguistic Computing, 2009, doi:10.1093/llc/fqp002.

  • T. Silander, The Most Probable Bayesian Network and Beyond. Doctoral dissertation, University of Helsinki. Department of Computer Science, Series of Publications A, Report A-2009-2.

  • T. Silander, T. Roos, and P. Myllymäki, Locally Minimax Optimal Predictive Modeling with Bayesian Networks. Pp. 504-511 in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, edited by D. van Dyk and M. Welling. JMLR Workshop and Conference Proceedings, Volume 5: AISTATS 2009.

  • T. Roos and B. Yu, Sparse Markov Source Estimation via Transformed Lasso, IEEE Information Theory Workshop (ITW-09), Volos, Greece, June 10–12, 2009.

  • T. Roos and B. Yu, Estimating Sparse Models from Multivariate Discrete Data via Transformed Lasso, Information Theory and Applications Workshop (ITA-09), San Diego CA, USA, February 8–13, 2009.

2008

  • T. Mononen and P. Myllymäki, On Recurrence Formulas for Computing the Stochastic Complexity. Pp. 281-286 in Proceedings of the 2008 International Symposium on Information Theory and its Applications (ISITA2008), December 7-10, 2008, Auckland, New Zealand.

  • A. Pernestål, H. Wettig, T. Silander, M. Nyberg and P. Myllymäki, A Bayesian Approach to Learning in Fault Isolation. Pp. 143-150 in Proceedings of The 19th International Workshop on Principles of Diagnosis (DX-08), edited by A. Grastien, W. Mayer, and M. Stumptner. September 22-24, 2008, Blue Mountains, NSW, Australia.

  • T. Silander, T. Roos, P. Kontkanen, and P. Myllymäki, Factorized NML Criterion for Learning Bayesian Network Structures. Pp. 257-264in Proceedings of the 4th European Workshop on Probabilistic Graphical Models (PGM-08), edited by M. Jaeger and T. Nielsen. September 17–19, Hirtshals, Denmark.

  • T. Mononen and P. Myllymäki, Computing the Multinomial Stochastic Complexity in Sub-Linear Time. Pp. 209-216 in Proceedings of the 4th European Workshop on Probabilistic Graphical Models (PGM-08), edited by M. Jaeger and T. Nielsen. September 17–19, Hirtshals, Denmark.

  • J. Heikkonen, I. Kontoiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus (eds), Proceedings of the First Workshop on Information Theoretic Methods in Science and Engineering. TICSP Series #43, Tampere International Center for Signal Processing, 2008.

  • P. Myllymäki, Recent Advances in Computing the NML for Discrete Bayesian Networks. In Proceedings of the First Workshop on Information Theoretic Methods in Science and Engineering, edited by J. Heikkonen, I. Kontoiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus. TICSP Series #43, Tampere International Center for Signal Processing, 2008.

  • J. Rissanen, Minimum Description Length, Scholarpedia, 3(8):6727, 2008.

  • T. Roos and J. Rissanen, On Sequentially Normalized Maximum Likelihood Models. In Proceedings of the First Workshop on Information Theoretic Methods in Science and Engineering, edited by J. Heikkonen, I. Kontoiannis, E. Liski, P. Myllymäki, J. Rissanen and I. Tabus. TICSP Series #43, Tampere International Center for Signal Processing, 2008.

  • P. Kontkanen and P. Myllymäki, An Empirical Comparison of NML Clustering Algorithms. In Proceedings of the 2008 International Conference on Information Theory and Statistical Learning (ITSL-08).

  • T. Mononen and P. Myllymäki, On the Multinomial Stochastic Complexity and its Connection to the Birthday Problem. In Proceedings of the 2008 International Conference on Information Theory and Statistical Learning (ITSL-08).

  • J. Perkiö, P. Myllymäki, V. Tuulos and P. Boda, Magrathea: A Mobile Agent- and Sensing Platform. In Proceedings of the 2008 International Conference on Wireless Networks (ICWN-08).

  • D. McAllester and P. Myllymäki (eds.), Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08), July 2008, Helsinki, Finland.

  • T. Roos, Monte Carlo Estimation of Minimax Regret with an Application to MDL Model Selection. Pp. 284-288 in Proceedings of the 2008 IEEE Information Theory Workshop. IEEE 2008.

  • T. Mononen and P. Myllymäki, Computing the NML for Bayesian Forests via Matrices and Generating Polynomials. Pp. 276-280 in Proceedings of the 2008 IEEE Information Theory Workshop. IEEE 2008.

  • T. Roos, T. Silander, P. Kontkanen, and P. Myllymäki, Bayesian Network Structure Learning using Factorized NML Universal Models. Pp. 272-276 in Proceedings of the 2008 Information Theory and Applications Workshop, San Diego, CA, January-February 2008.

  • P. Myllymäki, T. Roos, T. Silander, P. Kontkanen and H. Tirri, Factorized NML Models. Chapter 11 in Festschrift in Honour of Jorma Rissanen , edited by P. Grünwald, P. Myllymäki, I. Tabus, M. Weinberger and B. Yu.

  • P. Grünwald, P. Myllymäki, I. Tabus, M. Weinberger and B. Yu (eds.), Festschrift in Honor of Jorma Rissanen. TICSP Series #38, Tampere International Center for Signal Processing, 2008. Presented at the 2008 IEEE Information Theory Workshop.

2007

2006

  • K. Deforche, T. Silander, R. Camacho, Z . Grossman, M. A. Soares, K. Van Laethem, R. Kantor, Y. Moreau and A.-M. Vandamme, Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance. Bioinformatics, 22 (2006), 24 (December), 2975-2979.

  • S. Bloehorn, W. Buntine, A. Hotho, Editors' Introduction to the Special Issue "Learning in Web Search". Informatica, An International Journal of Computing and Informatics, 30 (2006) 2.

  • J. Fokker, J. Pouwelse, and W. Buntine, Tag-Based Navigation for Peer-to-Peer Wikipedia. Collaborative Web Tagging Workshop, 15th International World Wide Web Conference (WWW2006).

  • O.-P. Ryynänen, M. Puhakka, P. Myllymäki, P. Palomäki, V. Anttonen, R. Jukola and J. Takala, Sairaalaan lähettämisen arvointi Bayesin verkkomallilla. Finnish Medical Journal, Vol. 61 (2006), No. 51-52, 5353-5358.

  • J. Perkiö, V. Tuulos, M. Hermersdorf, H. Nyholm, J. Salminen and H. Tirri, Utilizing Rich Bluetooth Environments for Identity Prediction and Exploring Social Networks as Techniques for Ubiquitous Computing. Pp. 137-141 in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, edited by T. Nishida, Z. Shi, U. Visser, X. Wu, J. Liu, B. Wah, W. Cheung and Y.-M. Cheung. IEEE Computer Society, 2006.

  • M. Hermersdorf, H. Nyholm, J. Salminen, H. Tirri, J. Perkiö and V. Tuulos, Sensing in Rich Bluetooth Environments, Pp. 27-31 in Working Notes of the Workshop on World-Sensor-Web (WSW'2006), edited by P. Boda.

  • E. Soini, J. Martikainen, J. Lahtinen, P. Myllymäki, P. Kontkanen, K. Valtonen and O.-P. Ryynänen, Efficient Data Mining And Probabilistic Inference With P-Course: A Bayesian Method With Multilevel Priors For Medical Applications. The ISPOR 9th Annual European Congress Presentations in Value in Health: The Journal of the International Society for Pharmaeconomics and Outcomes Research, Vol.9 (2006), No. 6 (November/December), A270. .

  • M. Beigbeder, W. Buntine and W.-G. Yee (eds.), Proceedings of the Second Workshop on Open Source Information Retrieval (OSIR06).

  • J. Fokker, W. Buntine and J. Pouwelse, Tagging in Peer-to-Peer Wikipedia - A Method to Induce Cooperation. Pp. 39-45 in Proceedings of the Second Workshop on Open Source Information Retrieval (OSIR06), edited by M. Beigbeder, W. Buntine and W.-G. Yee.

  • W. Buntine, M. Taylor and F. Lagunas, Standards for Open Source Information Retrieval. Pp. 68-72 in Proceedings of the Second Workshop on Open Source Information Retrieval (OSIR06), edited by M. Beigbeder, W. Buntine and W.-G. Yee.

  • W.-G. Yee, M. Beigbeder and W. Buntine, SIGIR06 Workshop Report: Open Source Information Retrieval Systems. ACM SIGIR Forum 40 (2006) 2 (December), 61-65.

  • L. Zhou and W. Buntine, Web Search Technology - from Search to Semantic Search. In ASWC 2006 Workshops Proceedings, edited by G. Li, Y. LIang and M. Ronchetti. Jilin University Press, 2006.

  • P. Kontkanen and P. Myllymäki, Information-Theoretically Optimal Histogram Density Estimation. Technical Report HIIT-2006-2. Helsinki Institute for Information Technology, 2006.

  • T. Silander and P. Myllymäki, A Simple Approach for Finding the Globally Optimal Bayesian Network Structure. Pp. 445-452 in Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI-2006), edited by R. Dechter and T. Richardson. AUAI Press, 2006.

  • W. Buntine and A. Jakulin, Discrete Components Analysis. Pp. 1-33 in Subspace, Latent Structure and Feature Selection Techniques, edited by C. Saunders, M. Grobelnik, S. Gunn and J. Shawe-Taylor. Springer-Verlag 2006.

  • T. Roos, T. Heikkilä and P. Myllymäki, A Compression-Based Method for Stemmatic Analysis. Pp. 805-806 in Proceedings of the 17th European Conference on Artificial Intelligence (ECAI 2006), edited by G. Brewka, S. Coradeschi, A. Perini and P. Traverso. IOS Press, 2006.

  • J. Lahtinen, P. Myllymäki and O.-P. Ryynänen, P-Course: Medical Applications of Bayesian Classification with Informative Priors. In Proceedings of the ECAI'2006 Workshop on AI Techniques in Healthcare: Evidence-Based Guidelines and Protocols, edited by A. ten Teije, S. Miksch and P. Lucas.

  • W. Buntine and K. Valtonen, Topic Models in ALVIS. In Book of Abstracts of the International Workshop on Intelligent Information Access (IIIA-2006), Helsinki, FInland, July 2006.

  • V. Tuulos and A. Tuominen, Some Key Challenges in Web Crawlers and Content-Based Search Engines. In Book of Abstracts of the International Workshop on Intelligent Information Access (IIIA-2006), Helsinki, FInland, July 2006.

  • A. Tuominen and V. Tuulos, BulkFS: a Distributed Fault-Tolerant File System for Massive Data Application. In Book of Abstracts of the International Workshop on Intelligent Information Access (IIIA-2006), Helsinki, FInland, July 2006.

  • T. Roos, P. Grünwald, P. Myllymäki and H. Tirri, Generalization to Unseen Cases. Pp. 1129-1136 in Advances in Neural Information Processing Systems 18 (NIPS 05), edited by Y. Weiss, B.Schölkopf and J. Platt. MIT Press, Cambridge, MA, 2006.

  • M. Miettinen, J. Kurhila, P. Nokelainen and H. Tirri, Supporting Open-Ended Discourse with Transparent Groupware. International Journal of Web Based Communities, Vol. 2, No. 1, pp. 17-30.

  • M. Jaeger, J. Nielsen and T. Silander, Learning probabilistic decision graphs. International Journal of Approximate Reasoning, 42 (2006), 84-100.

2005

2004

2003

2002

2001

2000

  • T. Silander and H. Tirri, Model Selection for Bayesian Networks. In Proceedings of the Annual American Educational Research Association Meeting (AERA'00), SIG Educational Statisticians, New Orleans, 2000.

  • H. Tirri, T. Silander and P. Uronen, B-Course - a free Bayesian data analysis service. Proceedings of the NIPS'2000 Workshop on Software Support for Bayesian Analysis Systems. RIACS, NASA Ames Reseach Center, December 2000.

  • P. Kontkanen, J. Lahtinen, P. Myllymäki, T. Silander, and H. Tirri, Supervised Model-Based Visualization of High-Dimensional Data. Intelligent Data Analysis 4 (2000), 213-227.

  • P. Kontkanen, P. Myllymäki, H. Tirri and K. Valtonen, Bayesian Multinet Classifiers. In Proceedings of The 10th International Conference on Computing and Information (ICCI'2000). Kuwait, November 2000.

  • P. Kontkanen, J. Lahtinen, P. Myllymäki, and H. Tirri, An Unsupervised Bayesian Distance Measure. Pp. 148-160 in Advances in Case-Based Reasoning, Proceedings of the Fifth European Workshop on Case-Based Reasoning (EWCBR-2000), edited by E.Blanzieri and L.Portinale. Vol. 1898 in Lecture Notes in Artificial Intelligence, Springer-Verlag 2000.

  • P. Kontkanen, J. Lahtinen, P. Myllymäki, and H. Tirri, Unsupervised Bayesian Visualization of High-Dimensional Data. Pp. 325-329 in Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000), edited by R.Ramakrishnan, S.Stolfo, R.Bayardo and I.Parsa. The Association for Computing Machinery, New York, NY, USA, 2000.

  • P. Kontkanen, P. Myllymäki, H. Tirri and K. Valtonen, Classification with Bayesian Multinets. Pp. 134-146 in Proceedings of TOOLMET-2000, Symposium on Tool Environments and Development Methods for Intelligent Systems, edited by L.Yliniemi and E.Juuso. Oulun Yliopistopaino, 2000.

  • P. Kontkanen, P. Myllymäki, T. Silander, H. Tirri, and P. Grünwald, On Predictive Distributions and Bayesian Networks. Statistics and Computing 10 (2000), 39-54.

1999

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, Density Estimation by Minimum Encoding Mixtures of Histograms. Pp. 162-164 in Book of Abstracts, Second European Conference on Highly Structured Stochastic Systems (HSSS'99), Pavia, Italy, September 1999.

  • P. Myllymäki, Massively Parallel Probabilistic Reasoning with Boltzmann Machines. Applied Intelligence 11, 31-44 (1999).

  • P. Kontkanen, J. Lahtinen, P. Myllymäki, T. Silander, and H. Tirri, Using Bayesian Networks for Visualizing High-Dimensional Data. Pp. 38-47 in Proceedings of Pre- and Post-processing in Machine Learning and Data Mining: Theoretical Aspects and Applications, a workshop within Machine Learning and Applications (ACAI-99), edited by I.Bruha. Chania, Greece, July 1999.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, Urban Legends in Bayesian Network Research I: Model Selection for Supervised Problems. Arpakannus 1/99, 8-14.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, On the Accuracy of Stochastic Complexity Approximations. Chapter 9 in Causal Models and Intelligent Data Management, edited by A.Gammerman. Springer-Verlag, 1999.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, On Supervised Selection of Bayesian Networks. Pp. 334-342 in Proceedings of the 15th International Conference on Uncertainty in Artificial Intelligence (UAI'99), edited by K. Laskey and H. Prade. Morgan Kaufmann Publishers, 1999.

  • J. Lahtinen, P. Myllymäki, T. Silander, H. Tirri, and H.Wettig, An Empirical Evaluation of Stochastic Search Methods in Real-World Telecommunication Domains. Pp. 181-187 in Proceedings of the 3rd World Multiconference on Systemics, Cybernetics and Informatics (SCI'99) and 5th International Conference on Information Systems Analysis and Synthesis (ISAS'99), Volume 4, edited by M. Torres, B. Sanchez, S. Radhakrishan and R. Osers. International Institute of Information and Systemics, 1999.

  • P.Ruohotie, H. Tirri, P.Nokelainen and T. Silander, Modern Modeling of Professional Growth. Research Center for Vocational Education, Saarijärven Offset 1999.

  • H. Tirri, What the heritage of Thomas Bayes has to offer for modern educational research? Chapter II in P.Ruohotie, H. Tirri, P.Nokelainen and T. Silander, Modern Modeling of Professional Growth. Research Center for Vocational Education, Saarijärven Offset 1999.

  • T. Silander and H. Tirri, Bayesian classification. Chapter III in P.Ruohotie, H. Tirri, P.Nokelainen and T. Silander, Modern Modeling of Professional Growth. Research Center for Vocational Education, Saarijärven Offset 1999.

  • P. Nokelainen, P. Ruohotie and H. Tirri, Professional Growth Determinants-Comparing Bayesian and linear approaches to classification. Chapter IV in P.Ruohotie, H. Tirri, P.Nokelainen and T. Silander, Modern Modeling of Professional Growth. Research Center for Vocational Education, Saarijärven Offset 1999.

  • P. Kontkanen, P. Myllymäki, T. Silander, H. Tirri, K.Valtonen, Exploring the Robustness of Bayesian and Information-Theoretic Methods for Predictive Inference. Pp. 231-236 in Proceedings of Uncertainty'99: The Seventh International Workshop on Artificial Intelligence and Statistics, edited by D.Heckerman and J.Whittaker. Morgan Kaufmann Publishers, 1999.

1998

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, On Bayesian Case Matching. Pp. 13-24 in Advances in Case-Based Reasoning, Proceedings of the 4th European Workshop (EWCBR-98), edited by B.Smyth and P.Cunningham. Vol. 1488 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, BAYDA: Software for Bayesian Classification and Feature Selection. Pp. 254-258 in Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD-98), edited by R.Agrawal, P.Stolorz and G.Piatetsky-Shapiro. AAAI Press, Menlo Park, CA, 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, H. Tirri, and P. Grünwald, On the Small Sample Size Behavior of Bayesian and Information-Theoretic Approaches for Predictive Inference. Presented at the 6th Valencia International Meeting on Bayesian Statistics, Alcossebre, Spain, May-June 1998.

  • P. Grünwald, P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, Minimum Encoding Approaches for Predictive Modeling. Pp. 183-192 in Proceedings of the 14th International Conference on Uncertainty in Artificial Intelligence (UAI'98), edited by G.Cooper and S.Moral. Morgan Kaufmann Publishers, San Francisco, CA, 1998.

  • E. Koskimäki, J. Göös, P. Kontkanen, P. Myllymäki, and H. Tirri, Comparing Soft Computing Methods in Prediction of Manufacturing Data. Pp. 775-784 in Tasks and Methods in Applied Artificial Intelligence, Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA-98-AIE), edited by A.P. del Pobil, J.Mira and M. Ali. Vol. 1416 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1998.

  • H. Tirri and T. Silander, Stochastic complexity based estimation of missing elements in questionnaire data. Presented at the Annual American Educational Research Association Meeting (AERA'98), SIG Educational Statisticians, San Diego, 1998.

  • P. Myllymäki and H. Tirri, Prospects of Bayesian networks (in Finnish). Technology Report 58/98. Technology Development Center (TEKES), 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, Bayes Optimal Instance-Based Learning. Pp. 77-88 in Machine Learning: ECML-98, Proceedings of the 10th European Conference, edited by C.Nédellec and C.Rouveirol. Vol. 1398 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, H. Tirri, and P. Grünwald, Bayesian and Information-Theoretic Priors for Bayesian Network Parameters. Pp. 89-94 in Machine Learning: ECML-98, Proceedings of the 10th European Conference, edited by C.Nédellec and C.Rouveirol. Vol. 1398 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, Batch Classifications with Discrete Finite Mixtures. Pp. 208-213 in Machine Learning: ECML-98, Proceedings of the 10th European Conference, edited by C.Nédellec and C.Rouveirol. Vol. 1398 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri, Bayesian Classification and Feature Selection with BAYDA. In ECML-98: Demonstration and poster papers, edited by C.Nédellec and C.Rouveirol. CSR-98-07, Technische Universität Chemnitz, 1998.

  • P. Kontkanen, P. Myllymäki, T. Silander, H. Tirri, and P. Grünwald, A Comparison of non-informative priors for Bayesian networks. Pp. 53-62 in The Yearbook of the Finnish Statistical Society 1997, Hakapaino Oy, Helsinki 1998.

1997

1996

1995

1994

  • P. Orponen, Computational complexity of neural networks: A survey. Nordic Journal of Computing 1 (1994), 94-110.

  • P. Floréen, J. N. Kok, M. N. Rasch, Three methods for tracing a simple genetic algorithm. Pp. 148-158 in Proceedings of the 4th Belgian-Dutch Conference on Machine Learning (Benelearn 94, Rotterdam, June 1994), edited by J. C. Bioch and S. H. Nienhuys-Cheng. Erasmus University Rotterdam Report EUR-CS-94-05, Rotterdam 1994.

  • P. Floréen and J.N.Kok, Tracing the moments of distributions in genetic algorithms. Pp. 51-60 in Proceedings of the Second Finnish Workshop on Genetic Algorithms and their Applications (Vaasa, Finland, March 1994), edited by J.T. Alander. Report 94-2, University of Vaasa, 1994.

  • P. Myllymäki and H. Tirri, Learning Bayesian prototype trees by simulated annealing. Pp. 32-37 in Proceedings of the Conference on Artificial Intelligence Research in Finland (Turku, Finland, August 1994), edited by C.Carlsson, T.Järvi and T.Reponen. Finnish Artificial Intelligence Society, Helsinki 1994.

  • P. Myllymäki and H. Tirri, Massively parallel case-based reasoning with probabilistic similarity metrics. Pp. 144-154 in Topics in Case-Based Reasoning, edited by S.Wess, K.-D.Althoff and M.Richter. Lecture Notes in Artificial Intelligence, Volume 837. Springer Verlag, 1994.

  • P. Orponen, K. Ko, U. Schöning, O. Watanabe, Instance complexity. J. Assoc. Comput. Mach. 41 (1994), 96-121.

  • P. Floréen and T. Huuskonen, Uniqueness of maximum values in discrete distributions. Journal of Applied Probability 31 (September 1994).

  • H. Tirri and P. Myllymäki, MDL learning of probabilistic neural networks for discrete problem domains. Pp. 1493-1497 in Proceedings of the IEEE World Congress on Computational Intelligence (Orlando, June 1994).

  • P. Myllymäki and H. Tirri, Learning in neural networks with Bayesian prototypes. Pp. 60-64 in Proceedings of SOUTHCON'94 (Orlando, March 1994).

  • P. Myllymäki, Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines. Pp. 97-102 in Proceedings of SOUTHCON'94 (Orlando, March 1994).

  • S. Santos, Phase transitions in sparsely connected Boltzmann machines. Report C-1994-15, Department of Computer Science, University of Helsinki, 1994.

1993

  • P. Myllymäki, Bayesian reasoning by stochastic neural networks. Ph.Lic. Thesis, Report C-1993-67, Department of Computer Science, University of Helsinki, 1993.

  • P. Floréen, A short introduction to neural associative memories. Bulletin of the European Association for Theoretical Computer Science 51 (October 1993), 236-245.

  • P. Orponen, On the computational power of discrete Hopfield nets. Pp.215-226 in Proc. 20th Internat. Colloq. on Automata, Languages, and Programming (Lund, Sweden, July 1993). Lecture Notes in Computer Science 700, Springer-Verlag, Berlin Heidelberg, 1993.

  • P. Myllymäki and H. Tirri, Bayesian case-based reasoning with neural networks. Pp. 422-427 in Proceedings of the IEEE International Conference on Neural Networks (San Francisco, March 1993).

  • P. Floréen and P. Orponen, Attraction radii in binary Hopfield nets are hard to compute. Neural Computation 5 (1993), 812-821.

1992

  • P. Floréen, Computational complexity problems in neural associative memories. Ph. D. Thesis, Report A-1992-5, Department of Computer Science, University of Helsinki, 1992.

  • P. Floréen, Neuraaliset assosiatiivimuistit. Tietojenkäsittelytiede 3 (1992), 44-47.

  • P. Floréen, P. Myllymäki, P. Orponen, and H. Tirri, Neula: A hybrid neural-symbolic expert system shell. Tietojenkäsittelytiede 3 (1992), 11-18.

  • P. Floréen, A new associative memory model. International Journal of Intelligent Systems 7 (1992), 455-467.

  • P. Orponen, Neural networks and complexity theory (invited talk). Pp. 50-61 in Proc. of the 17th Internat. Symp. on Mathematical Foundations of Computer Science (Prague, Czechoslovakia, August 1992). Lecture Notes in Computer Science 629, Springer-Verlag, Berlin Heidelberg, 1992.

  • P. Myllymäki, P. Orponen, and T. Silander, Integrating symbolic reasoning with neurally represented background knowledge. Pp. 231-240 in Proceedings of the Finnish AI Conference (Espoo, Finland, June 1992), edited by E. Hyvönen, J. Seppänen and M. Syrjänen. Finnish AI Society, Helsinki, 1992.

1991

  • P. Myllymäki and P. Orponen, Programming the harmonium. Pp. 671-677 in Proceedings of the International Joint Conference on Neural Networks (Singapore, November 1991).

  • P. Floréen, Worst-case convergence times for Hopfield memories. IEEE Transactions on Neural Networks 2 (1991), 533-535.

  • P. Floréen, The convergence of Hamming memory networks. IEEE Transactions on Neural Networks, 2 (1991),449-457.

  • H. Tirri, Concept randomness and neural networks. Pp. 1367-1370 in Proceedings of the International Conference on Artificial Neural Networks (Espoo, Finland, June 1991), edited by T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas. Elsevier Science Publishers, Amsterdam 1991.

1990

  • H. Tirri, Implementing Expert System Rule Conditions by Neural Networks. New Generation Computing 10 (1991), 55-71. Also: TR-1050, Department of Computer Sciences, Purdue University, 1990.

  • P. Floréen, P. Myllymäki, P. Orponen, and H. Tirri, Compiling object declarations into connectionist networks. AI Communications 3 (1990) 4 (December), 172-183.

  • P. Floréen, Computational complexity issues in neural associative memories. Ph.Lic Thesis, Report C-1990-40, Department of Computer Science, University of Helsinki, 1990.

  • R.J.T. Morris, L.D. Rubin, and H. Tirri, Neural network techniques for object orientation detection: Solution by optimal feedforward network and learning vector quantization approaches. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 11 (November), 1107-1115.

  • P. Orponen, P. Floréen, P. Myllymäki, and H. Tirri, A neural implementation of conceptual hierarchies with Bayesian reasoning. Pp. 297-303 in Proceedings of the International Joint Conference on Neural Networks (San Diego, CA, June 1990). IEEE, New York, 1990.

  • P. Floréen, An analysis of the convergence time of Hamming memory networks. Pp. 867-872 in Proceedings of the International Joint Conference on Neural Networks (San Diego, CA, June 1990). IEEE, New York, 1990.

  • P. Orponen, P. Floréen, P. Myllymäki, and H. Tirri, A neural implementation of Bayesian reasoning. Pp. 277-287 in Proceedings of the Finnish Artificial Intelligence Symposium (Oulu, Finland, June 1990), edited by P. Salonen M. Djupsund and M. Syrjänen. Finnish Artificial Intelligence Society, 1990.

  • P. Myllymäki, H. Tirri, P. Floréen, and P. Orponen, Compiling high-level specifications into neural networks. Pp. 475-478 in Proceedings of the International Joint Conference on Neural Networks (Washington D.C., January 1990). IEEE, New York, 1990.

  • P. Orponen, Dempster's rule of combination is #P-complete. Artificial Intelligence 44 (1990), 245-253.

1989

  • P. Floréen and P. Orponen, On the computational complexity of analyzing Hopfield nets. Complex Systems 3 (1989), 577-587.

  • P. Floréen, P. Myllymäki, P. Orponen, and H. Tirri, Neural representation of concepts for robust inference. Pp. 89-98 in Proceedings of the International Symposium Computational Intelligence II (Milano, Italy, September 1989), edited by F. Gardin and G. Mauri. Elsevier Science Publishers, Amsterdam, 1989.

  • H. Tirri, Applying neural computing to expert system design. Part I: Coping with complex sensory data and attribute selection. Pp. 474-488 in Proceedings of the 3rd International Conference on Data Organization and Algorithms (Paris, France, June 1989) edited by W. Litwin and H.-J. Schek. Springer-Verlag, Berlin Heidenberg, 1989.

  • H. Tirri, Neural information processing and applications. Tutorial at the 3rd International Conference on Data Organization and Algorithms (Paris, France, June 1989). Institut National de Recherche en Informatique et en Automatique (INRIA), Le Chesnay Cedex, France, 1989.

  • P. Orponen, An experimental evaluation of the optimal capacity of the Hopfield associative memory. Page 612 in Proceedings of the International Joint Conference on Neural Networks (Washington, D.C., June 1989). IEEE, New York, 1989.

  • P. Floréen and P. Orponen, Counting stable states and sizes of attraction domains in Hopfield nets is hard. Pp. 395-399 in Proceedings of the International Joint Conference on Neural Networks (Washington, D.C, June 1989). IEEE, New York, 1989.

  • R.J.T. Morris, L.D. Rubin, and H. Tirri, A comparison of feedforward and self-organizing approaches to the font orientation problem. Pp. 291-298 in Proceedings of the International Joint Conference on Neural Networks (Washington, D.C., June 1989). IEEE, New York, 1989.

  • H. Tirri, Feedforward and learning vector quantization approaches for font orientation detection. In Nordic Symposium on Neural Computing (Hanasaari, Finland, April 1989).


Last updated on 10 Jun 2017 by Teemu Roos - Page created on 18 Sep 2012 by Petri Myllymäki