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Year 2003

  1. Stefano Merler; Cesare Furlanello; Barbara Larcher; Andrea Sboner,
    Automatic model selection in cost-sensitive boosting,
    in «INFORMATION FUSION»,
    vol. 4,
    n. 1,
    2003
    , pp. 3 -
    10
  2. Stefano Merler; Bruno Caprile; Cesare Furlanello,
    Bias-Variance Control via Hard Points Shaving,
    in «INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE»,
    vol. 18,
    2003
  3. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    in «BMC BIOINFORMATICS»,
    2003
    , pp. 54 -
    73
  4. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    An accelerated procedure for recursive feature ranking on microarray data,
    ADVANCES IN NEURAL NETWORK RESEARCH: IJCNN 2003,
    Elsevier,
    2003
    , pp. 641 -
    648
  5. Cesare Furlanello; Markus Neteler; Stefano Merler; Stefano Menegon; A. Fontanari; Angela Donini; Annapaola Rizzoli; C. Chemini,
    Distributed Statistical Computing,
    2003
    , (Distributed Statistical Computing,
    Vienna, Austria,
    21/03/2003 - 22/03/2003)
  6. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    Control of selection bias in microarray data analysis,
    Biotecnologica. A journal on Biotechnology and Molecular Biology,
    2003
    , pp. 217-
    222
  7. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    Gene selection and classification by entropy-based recursive feature elimination,
    Proceedings of the International Joint Conference on Neural Networks 2003,
    2003
    , pp. 3077-
    3082
  8. Bruno Caprile; Stefano Merler; Cesare Furlanello,
    Exact Bagging with k-Nearest Neighbour Classifiers,
    A formula is derived for the exact computation of Bagging classifiers when the base model adopted is k-Nearest Neighbour (k-NN). The formula, that holds in any dimension and does not require the extraction of bootstrap replicates, proves that Bagging cannot improve 1-Nearest Neighbour. It also proves that, for k > 1, Bagging has a smoothing effect on k-NN. Convergence of empirically bagged k-NN predictors to the exact formula is also considered. Efficient approximations to the exact formula are derived, and their applicability to practical cases is illustrated,
    2003
  9. Stefano Merler; Cesare Furlanello,
    Machine learning on historic air photographs for mapping risk of unexploded bombs,
    2003
  10. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    An accelerated procedure for recursive feature ranking on microarray data,
    We describe a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE (Entropy-based Recursive Feature Elimination) method eliminates chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. With specific regard to DNA microarray datasets, the method is designed to support computationally intensive model selection in classification problems in which the number of features is much larger than the number of samples. We test E-RFE on synthetic and real data sets, comparing it with other SVM-based methods. The speed-up obtained with E-RFE supports predictive modeling on high dimensional microarray data,
    2003

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