J. S. Sanchez-rekin lankidetzan egindako argitalpenak (14)

2006

  1. Complexity reduction in efficient prototype-based classification

    Pattern Recognition

2005

  1. Decision boundary preserving prototype selection for nearest neighbor classification

    International Journal of Pattern Recognition and Artificial Intelligence, Vol. 19, Núm. 6, pp. 787-806

  2. Imbalanced training set reduction and feature selection through genetic optimization

    Frontiers in Artificial Intelligence and Applications

2004

  1. Forgetting superfluous information in supervised pattern recognition systems with ongoing learning

    Tendencias de la minería de datos en España: Red Española de Minería de Datos y Aprendizaje (TIC2002-11124-E) (Raúl Giráldez), pp. 109-118

  2. The Imbalanced Training Sample Problem: Under or over Sampling?

    Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 Proceedings

2003

  1. Learning from imbalanced sets through resampling and weighting

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2652, pp. 80-88

  2. Restricted decontamination for the imbalanced training sample problem

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2905, pp. 424-431

2002

  1. On filtering the training prototypes in nearest neighbour classification

    Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

1999

  1. Learning vector quantization with alternative distance criteria

    Proceedings - International Conference on Image Analysis and Processing, ICIAP 1999

1998

  1. Improving the k-NCN classification rule through heuristic modifications

    Pattern Recognition Letters, Vol. 19, Núm. 13, pp. 1165-1170

1997

  1. On the equivalency between decision tree classifiers and the nearest neighbour rule

    CAEPIA'97: actas

  2. On the use of neighbourhood-based non-parametric classifiers

    Pattern Recognition Letters, Vol. 18, Núm. 11-13, pp. 1179-1186

  3. Prototype selection for the nearest neighbour rule through proximity graphs

    Pattern Recognition Letters, Vol. 18, Núm. 6, pp. 507-513

  4. Using proximity and spatial homogeneity in neighbourhood-based classifiers

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)