Now showing items 1-2 of 2

  • Scaling up semi-supervised learning: An efficient and effective LLGC variant

    Pfahringer, Bernhard; Leschi, Claire; Reutemann, Peter (Springer, Berlin, 2007)
    Domains like text classification can easily supply large amounts of unlabeled data, but labeling itself is expensive. Semi- supervised learning tries to exploit this abundance of unlabeled training data to improve ...
  • Using weighted nearest neighbor to benefit from unlabeled data

    Driessens, Kurt; Reutemann, Peter; Pfahringer, Bernhard; Leschi, Claire (Springer, Berlin, 2006)
    The development of data-mining applications such as textclassification and molecular profiling has shown the need for machine learning algorithms that can benefit from both labeled and unlabeled data, where often the ...

Claire Leschi has 3 co-authors in Research Commons.