Supervised classifier combination through Generalized Additive multi-model

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

Volumn 1857 LNCS, Issue , 2000, Pages 167-176

  Conversano, Claudio ^a   Siciliano, Roberta ^a   Mola, Francesco ^b  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; STATISTICAL METHODS;

ESTIMATION PROCEDURES; GENERALIZED ADDITIVE MODEL; GENERALIZED ADDITIVES; MULTIPLE CLASSIFIER COMBINATION; NON-PARAMETRIC MODEL; SCORING ALGORITHMS; SUPERVISED CLASSIFICATION; SUPERVISED CLASSIFIERS;

ADDITIVES; ALGORITHMS;

EID: 84867069868     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45014-9_16     Document Type: Conference Paper

References (16)

1. Antoch, J., Mola, F.: Parsimonious regressograms for generalized additive models. In N.C. Lauro (Ed.): Sviluppi Metodologici Applicativi dell'Inferenza Computazionale nell'Analisi Multidimensionale dei Dati, (1996), Rocco Curto, Napoli, 46-59.
2. Breiman, L.: Bagging Predictors. Machine Learning, 26, (1996), 46-59.
3. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J.: Classification and Regression Trees, Belmont C.A. Wadsworth, (1984).
4. Cleveland, W. S.: Robust locally weighted regression and smoothing scatterplots, Journal of the American Statistical Association, 74, (1979), 829-836.
5. Conversano, C.: A Regression Tree Procedure for Smoothing in Generalized Additive Models. In M. Huskova et al. (eds.): Prague Stochastics'98 Abstracts, 13-14, Union of Czech Mathematicians and Physicists, (1998).
6. Conversano, C.: Semiparametric Models for Supervised Classification and Prediction. Some Proposals for Model Integration and Estimation Procedures (in Italian), Ph.D Thesis in Computational Statistics and Data Analysis, Universitá di Napoli Federico II, (2000).
7. Conversano, C., Siciliano, R.: Modelling MIB30 Index Volatility: Regression Tree Criteria for Smoothing and Variable Selection in Generalized Additive Models, submitted.
8. Friedman, J.H.: Multivariate Adaptive Regression Splines. The Annals of Statistics, 23, (1991), 1-149.
9. Hastie, T.J., and Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, London, (1990).
10. Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis. Academic Press, London, (1995).
11. Mola, F.: Selection of Cut Points in Generalized Additive Models. In: Classification and Data Analysis: Theory and Application, Springer Verlag, Berlin, (1998), 121-128.
12. Mola, F., Siciliano, R.: A Fast Splitting Procedure for Classification Trees, Statistics and Computing, 7, (1997), 208-216.
13. Siciliano, R.: Exploratory versus Decision Trees, in R. Payne, P. Green (Eds.): Proceedings in Computational Statistics: COMPSTAT '98, Physica-Verlag, Heidelberg (D), (1998), 113-124.
14. Siciliano, R., Mola, F: Multivariate Data Analysis through Classification and Regression Trees, Computational Statistics and Data Analysis, 32, Elsevier Science, (2000), 285-301.
15. Silvermann B. W.: Some aspects of the spline smoothing approach to non-parametric regression curve fitting, Journal of the Royal Statistical Society, B 47, (1985) 1-52.
16. Yee, T.W., Wild, C.J.: Vector generalized additive models, Journal of Royal Statistical Society, B 58, (1996), 481-493.