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  1. Article

    Open Access

    Scalable variable selection for two-view learning tasks with projection operators

    In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where...

    Sandor Szedmak, Riikka Huusari, Tat Hong Duong Le, Juho Rousu in Machine Learning (2024)

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  2. Article

    Multi-view kernel completion

    In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, with help of information from other i...

    Sahely Bhadra, Samuel Kaski, Juho Rousu in Machine Learning (2017)

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  3. Article

    Multilabel classification through random graph ensembles

    We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel, and a kernel-based structured output learner as the base cl...

    Hongyu Su, Juho Rousu in Machine Learning (2015)

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  4. No Access

    Article

    Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates

    We consider multisplitting of numerical value ranges, a task that is encountered as a discretization step preceding induction and also embedded into learning algorithms. We are interested in finding the partit...

    Tapio Elomaa, Juho Rousu in Data Mining and Knowledge Discovery (2004)

  5. No Access

    Article

    Linear-Time Preprocessing in Optimal Numerical Range Partitioning

    Only a subset of the boundary points—the segment borders—have to be taken into account in searching for the optimal multisplit of a numerical value range with respect to the most commonly used attribute evalua...

    Tapio Elomaa, Juho Rousu in Journal of Intelligent Information Systems (2002)

  6. Article

    General and Efficient Multisplitting of Numerical Attributes

    Often in supervised learning numerical attributes require special treatment and do not fit the learning scheme as well as one could hope. Nevertheless, they are common in practical tasks and, therefore, need t...

    Tapio Elomaa, Juho Rousu in Machine Learning (1999)

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