You are here

Year 2003

  1. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    in «BMC BIOINFORMATICS»,
    2003
    , pp. 54 -
    73
  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. 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
  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; 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
  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; 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)
  8. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    Gene selection and classification by entropy-based recursive feature elimination,
    We analyse E-RFE (Entropy-based Recursive Feature Elimination), a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE method operates the elimination of chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. The method is designed to support computationally intensive model selection in classification problems in which the number of features is much largerthan the number of samples. We proofread the elimination procedure on synthetic data sets, and we demonstrate the applicability of E-RFE for the identification of biomedically important genes in predictive classification of microarray data,
    2003
  9. 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
  10. Stefano Merler; Cesare Furlanello,
    Machine learning on historic air photographs for mapping risk of unexploded bombs,
    2003

Pages