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Year 2005

  1. G. Jurman,
    Graded Lie algebras of maximal class, III.,
    in «JOURNAL OF ALGEBRA»,
    vol. 284,
    n. 2,
    2005
    , pp. 435 -
    461
  2. P. Fateh-Moghadam; G. Dallago; S. Piffer; G. Zanon; S. Menegon; S. Fontanari; C. Furlanello.,
    Epidemiology of Road Traffic Accidents in the province of Trento: first results of an integrated surveillance system (MITRIS),
    in «EPIDEMIOLOGIA E PREVENZIONE»,
    vol. 29,
    n. 3/4,
    2005
    , pp. 172 -
    179
  3. C. Furlanello; M. Serafini; S. Merler; G. Jurman,
    Semisupervised Learning for Molecular Profiling,
    in «IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS»,
    2005
    , pp. 110 -
    118
  4. M. Neteler; D. Grasso; I. Michelazzi; L. Miori; S. Merler; C. Furlanello,
    in «INTERNATIONAL JOURNAL OF GEOINFORMATICS»,
    2005
    , pp. 51 -
    61
  5. S. Merler; C. Furlanello; G. Jurman.,
    Machine learning on historic air photographs for mapping risk of unexploded bombs,
    Image Analysis and Processing – ICIAP 2005 Image Analysis and Processing – ICIAP 2005,
    Berlino,
    Springer,
    2005
    , pp. 735 -
    742
  6. S. Merler; G. Jurman,
    Terminated Ramp: a data-driven kernel,
    In this paper we propose a novel data–driven kernel automatically determined by the training examples. Basically, it is built by combining a finite set of linearly independent functions, namely generalized terminated ramp functions, each depending on a pair of training data. When working in the Tikhonov regularization framework, the unique free parameter to be optimized is the regularizer, representing a trade-off between empirical error and smoothness of the solution,
    2005
  7. C. Furlanello; S. Merler; G. Jurman,
    Combining feature selection and DTW for time-varying functional genomics,
    Given temporal high-throughput data defining a two-class functional genomic process, feature selection algorithms may be applied to extract a panel of discriminating gene time series from noise. We then aim at identifying the main trends of activity along the process: a reconstruction method based on stagewise boosting is endowed with a similarity measure based on the Dynamic Time Warping algorithm, defining a ranked set of time series component most contributing to the reconstruction.
    The approach is applied on synthetic and public microarray data. On the Cardiogenomics PGA mouse model of Myocardial Infarction, the approach allows the identification of a timevarying molecular profile of the ventricular remodeling process
    ,
    2005