MACHINE LEARNING

Research outputs

  1. 2018
  2. E-pub ahead of print

    Inverse reinforcement learning from summary data

    Kangasrääsiö, A. & Kaski, S. 27 Jun 2018 In : Machine Learning.

    Research output: Contribution to journalArticle

  3. 2017
  4. Published
  5. Published

    Multi-view kernel completion

    Bhadra, S., Kaski, S. & Rousu, J. May 2017 In : Machine Learning. 106, 5, p. 713–739

    Research output: Contribution to journalArticle

  6. Published

    Adaptive edge weighting for graph-based learning algorithms

    Karasuyama, M. & Mamitsuka, H. 2017 In : Machine Learning. 106, 2, p. 307-335

    Research output: Contribution to journalArticle

  7. 2016
  8. Published

    Bayesian multi-tensor factorization

    Khan, S., Leppäaho, E. & Kaski, S. Nov 2016 In : Machine Learning. 105, 2, p. 233-253 21 p.

    Research output: Contribution to journalArticle

  9. Published

    Explaining mixture models through semantic pattern mining and banded matrix visualization

    Adhikari, P. R., Vavpetič, A., Kralj, J., Lavrač, N. & Hollmén, J. Oct 2016 In : Machine Learning. 105, 1, p. 3-39 36 p.

    Research output: Contribution to journalArticle

  10. Published

    Learning undirected graphical models using persistent sequential Monte Carlo

    Xiong, H., Szedmak, S. & Piater, J. May 2016 In : MACHINE LEARNING. 103, 2, p. 239-260 22 p.

    Research output: Contribution to journalArticle

  11. Published

    Probabilistic archetypal analysis

    Seth, S. & Eugster, M. J. A. Jan 2016 In : MACHINE LEARNING. 102, 1, p. 85-113 29 p.

    Research output: Contribution to journalArticle

  12. 2015
  13. Published

    Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track

    Bielza, C., Gama, J., Jorge, A. & Zliobaite, I. 21 Jul 2015 In : Machine Learning. 100, 2-3, p. 157-159 3 p.

    Research output: Contribution to journalEditorial

  14. Published

    Information retrieval approach to meta-visualization

    Peltonen, J. & Lin, Z. 1 May 2015 In : Machine Learning. 99, 2, p. 189-229 41 p.

    Research output: Contribution to journalArticle

  15. Published

    Evaluation methods and decision theory for classification of streaming data with temporal dependence

    Zliobaite, I., Bifet, A., Read, J., Pfahringer, B. & Holmes, G. 2015 In : MACHINE LEARNING. 98, 3, p. 455-482

    Research output: Contribution to journalArticle

  16. Published

    Multilabel Classification through Random Graph Ensembles.

    Su, H. & Rousu, J. 2015 In : MACHINE LEARNING. 99, 2, p. 231-256

    Research output: Contribution to journalArticle

  17. Published

    Optimizing regression models for data streams with missing values

    Zliobaite, I. & Hollmen, J. 2015 In : MACHINE LEARNING. 99, 1, p. 47-73

    Research output: Contribution to journalArticle

  18. 2012
  19. Published

    Focused multi-task learning in a Gaussian process framework

    Leen, G., Peltonen, J. & Kaski, S. 2012 In : MACHINE LEARNING. 89, 1-2, p. 157-182

    Research output: Contribution to journalArticle

  20. 2011
  21. Published

    Introduction to the special issue on mining and learning with graphs

    Vishwanathan, S. V. N., Kaski, S., Neville, J. & Wrobel, S. Feb 2011 In : Machine Learning. 82, 2, p. 91-93 3 p.

    Research output: Contribution to journalEditorial

  22. Published

    Multi-way set enumeration in weight tensors

    Georgii, E., Tsuda, K. & Schölkopf, B. Feb 2011 In : Machine Learning. 82, 2, p. 123-155 33 p.

    Research output: Contribution to journalArticle

  23. 2010
  24. Published

    Infinite factorization of multiple non-parametric views

    Rogers, S., Klami, A., Sinkkonen, J., Girolami, M. & Kaski, S. 2010 In : MACHINE LEARNING. 79, 1-2, p. 201-226

    Research output: Contribution to journalArticle

  25. 2009
  26. Published

    Latent Grouping Models for User Preference Prediction

    Savia, E., Puolamäki, K. & Kaski, S. 2009 In : Machine Learning. 74, p. 75-109

    Research output: Contribution to journalArticle

  27. 2005
  28. Published

    On Discriminative Bayesian Network Classifiers and Logistic Regression

    Roos, T., Wettig, H., Grunwald, P., Myllymäki, P. & Tirri, H. 2005 In : Machine Learning. 59, 3, p. 267-296

    Research output: Contribution to journalArticle

ID: 332680