Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
How Computational Statistics Became the Backbone of Modern Data Science Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

33. Bagging, Boosting and Ensemble Methods

verfasst von : Peter Bühlmann

Erschienen in: Handbook of Computational Statistics

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principle of ensemble methods is to construct a linear combination of some model fitting method, instead of using a single fit of the method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Saddlepoint Approximations: A Review and Some New Applications
Nächstes Kapitel Heavy-Tailed Distributions in VaR Calculations
Literatur
Allwein, E., Schapire, R., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2001)MathSciNetMATH
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9, 1545–1588 (1997)CrossRef
Audrino F., Barone-Adesi G.: A multivariate FGD technique to improve VaR computation in equity markets. Comput. Manag. Sci. 2, 87–106 (2005)MATHCrossRef
Audrino, F., Bühlmann, P.: Volatility estimation with functional gradient descent for very high-dimensional financial time series. J. Comput. Fin, 6(3), 65–89 (2003)
Bartlett, P.L.: Prediction algorithms: complexity, concentration and convexity. In: Proceedings of the 13th IFAC Symposium on System Identification, pp. 1507–1517 (2003)
Bartlett, P.L., Jordan, M.I., McAuliffe, J.D.: Convexity, classification, and risk bounds. J. Am. Stat. Assoc. 101, 138–156 (2006)MathSciNetMATHCrossRef
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting and variants. Mach. Learn. 36, 1545–1588 (1999)CrossRef
Biau, G., Devroye, L. Lugosi, G.: Consistency of Random Forests and other averaging classifiers. J. Mach. Learn. Res. 9, 2015–2033 (2008)MathSciNetMATH
Benner, A.: Application of “aggregated classifiers” in survival time studies. In: Härdle, W., Rönz, B. (eds.) In: COMPSTAT 2002 – Proceedings in Computational Statistics – 15th Symposium held in Physika, Heidelberg, Berlin (2002)
Borra, S., Di Ciaccio, A.: Improving nonparametric regression methods by bagging and boosting. Comput. Stat. Data Anal. 38, 407–420 (2002)MATHCrossRef
Breiman, L.: Prediction games & arcing algorithms. Neu. Comput. 11, 1493–1517 (1999)CrossRef
Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)MATH
Bühlmann, P.: Bagging, subagging and bragging for improving some prediction algorithms. In: Akritas, M.G., Politis, D.N. (eds.) In: Recent Advances and Trends in Nonparametric Statistics, Elsevier, Amsterdam (2003)
Bühlmann, P.: Boosting for high-dimensional linear models. Ann. Stat. 34, 559–583 (2006)MATHCrossRef
Bühlmann, P., Hothorn, T.: Boosting algorithms: regularization, prediction and model fitting (with discussion). Stat. Sci. 22, 477–505 (2007)MATHCrossRef
Bühlmann, P., Hothorn, T.: Twin Boosting: improved feature selection and prediction. Stat. Comput. 20, 119–138 (2010)MathSciNetCrossRef
Bühlmann, P., Yu, B: Discussion on Additive logistic regression: a statistical view of boosting (Auths. Friedman, J., Hastie, T., Tibshirani, R.) Ann. Stat. 28, 377–386 (2000)
Bühlmann, P., Yu, B.: Analyzing bagging. Ann. Stat. 30, 927–961 (2002)MATH
Bühlmann, P., Yu, B.: Boosting with the L 2loss: regression and classification. J. Am. Stat. Assoc. 98, 324–339 (2003)MATHCrossRef
Bylander, T.: Estimating generalization error on two-class datasets using out-of-bag estimates. Mach. Learn. 48, 287–297 (2002)MATHCrossRef
Chen, S.X., Hall, P.: Effects of bagging and bias correction on estimators defined by estimating equations. Statistica Sinica 13, 97–109 (2003)MathSciNetMATH
Dettling, M.: BagBoosting for tumor classification with gene expression data. Bioinformatics 20 (18), 3583–3593 (2004).CrossRef
Dettling, M., Bühlmann, P.: Boosting for tumor classification with gene expression data. Bioinformatics 19(9), 1061–1069 (2003)CrossRef
Dudoit, S., Fridlyand, J.: Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19(9), 1090–1099 (2003)CrossRef
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression (with discussion). Ann. Stat. 32, 407–451 (2004)MathSciNetMATHCrossRef
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In Machine Learning: Proceedings of 13th International Conference, pp. 148–156. Morgan Kauffman, San Francisco (1996)
Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–141 (1991)MATH
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)MATHCrossRef
Friedman, J.H., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28, 337–407 (2000)MathSciNetMATHCrossRef
Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman & Hall, London (1990)MATH
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer, New York (2001)
Hothorn, T., Bühlmann, P., Kneib, T., Schmid M., Hofner, B.: Model-based boosting 2.0. Journal of Machine Learning Research 11, 2109–2113 (2010).
Hurvich, C.M., Simonoff, J.S., Tsai, C.-L.: Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J. Roy. Stat. Soc. B 60, 271–293 (1998)MathSciNetMATHCrossRef
Jiang, W.: Process consistency for AdaBoost (with discussion). Ann. Stat. 32, 13–29, (disc. pp. 85–134) (2004)
Leitenstorfer, F., Tutz, G.: Generalized monotonic regression based on B-splines with an application to air pollution data. Biostatistics 8, 654–673 (2007)MATHCrossRef
Li, Y., Jeon, Y.: Random Forests and adaptive nearest neighbors. J. Am. Stat. Assoc. 101, 578–590 (2006)CrossRef
Lugosi, G., Vayatis, N.: On the Bayes-risk consistency of regularized boosting methods. Ann. Stat. 32, 30–55 (disc. pp. 85–134) (2004)
Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41, 3397–3415 (1993)MATHCrossRef
Mannor, S., Meir, R., Zhang, T.: The consistency of greedy algorithms for classification. Proceedings COLT02, Vol. 2375 of LNAI, pp. 319–333. Springer, Sydney (2002)
Mason, L., Baxter, J., Bartlett, P., Frean, M.: Functional gradient techniques for combining hypotheses. In: Smola, A.J., Bartlett, P.J., Schölkopf, B., Schuurmans, D. (eds.) In: Advances in Large Margin Classifiers MIT Press, Cambridge, MA (2000)
Meinshausen, N., Bühlmann, P.: High-dimensional graphs and variable selection with the Lasso. Ann. Stat. 34, 1436–1462 (2006)MATHCrossRef
Meinshausen, N., Bühlmann, P.: Stability selection (with discussion). Journal of the Royal Statistical Society: Series B, 72, 417–473 (2010).CrossRef
Meinshausen, N., Meier, L., Bühlmann, P.: p-values for high-dimensional regression. J. Am. Stat. Assoc. 104, 1671–1681 (2009)MATHCrossRef
Ridgeway, G.: Looking for lumps: Boosting and bagging for density estimation. Comput. Stat. Data Anal. 38(4), 379–392 (2002)MathSciNetMATHCrossRef
Rosset, S., Zhu, J., Hastie, T.: Boosting as a regularized path to a maximum margin classifier. J. Mach. Learn. Res. 5, 941–973 (2004)MathSciNetMATH
Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5, 197–227 (1990)
Schapire, R.E.: The boosting approach to machine learning: an overview. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds.) In: MSRI Workshop on Nonlinear Estimation and Classification. Springer, New York (2002)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinformatics 9(307), 1–11 (2008)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58, 267–288 (1996)MathSciNetMATH
Tukey, J.W.: Exploratory data analysis. Addison-Wesley, Reading, MA (1977)MATH
Tutz, G., Hechenbichler, K.: Aggregating classifiers with ordinal response structure. J. Stat. Comput. Simul. 75, 391–408 (2005)MathSciNetMATHCrossRef
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)MATH
Wahba, G.: Spline Models for Observational Data. Society for Industrial and Applied Mathematics (1990)
Zhang, T., Yu, B.: Boosting with early stopping: convergence and consistency. Ann. Stat. 33, 1538–1579 (2005)MATHCrossRef
Zhao, P., Yu, B.: On model selection consistency of Lasso. J. Mac. Learn. Res. 7, 2541–2563 (2006)MathSciNetMATH
Zhu, J., Rosset, S., Hastie, T., Tibshirani, R.: 1-norm support vector machines. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, 49–56 (2004)
Zou, H.: The adaptive Lasso and its oracle properties. J. Am. Stat. Assoc. 101, 1418–1429 (2006)MATHCrossRef
Metadaten
Titel
Bagging, Boosting and Ensemble Methods
verfasst von
Peter Bühlmann
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Handbook of Computational Statistics

Print ISBN: 978-3-642-21550-6

Electronic ISBN: 978-3-642-21551-3

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-642-21551-3_33

Premium Partner

    Bildnachweise