Showing items with similar title or keywords.

• Feature extraction for supervised learning in knowledge discovery systems 

  Pechenizkiy, Mykola (University of Jyväskylä, 2005)

  Tiedon louhinnalla pyritään paljastamaan tietokannasta tietomassaan sisältyviä säännönmukaisuuksia, joiden olemassaolosta ei vielä olla tietoisia. Kun tietokantaan sisältyvät tiedot ovat kovin moniulotteisia, yksittäisten ...

• Diversity in Search Strategies for Ensemble Feature Selection 

  Tsymbal, Alexey; Pechenizkiy, Mykola; Cunningham, Padraig (Elsevier, 2005)

  Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown ...

• Comparison of feature importance measures as explanations for classification models 

  Saarela, Mirka; Jauhiainen, Susanne (Springer, 2021)

  Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature ...

• Updating strategies for distance based classification model with recursive least squares 

  Raita-Hakola, Anna-Maria; Pölönen, Ilkka (Copernicus Publications, 2022)

  The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the ...

• Dynamic integration of data mining methods in knowledge discovery systems 

  Tsymbal, Alexey (University of Jyväskylä, 2002)