Decision-theoretic Generalization Of Online Learning And An Application To Boosting

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@MISC{Freund96adecision-theoretic, author = {Yoav Freund and Robert E. Schapire}, title = { A Decision-Theoretic …

(1995). Boosting a weak learning algorithm by majority. Inform. and Comput. 121 256-285]. Schapire and Freund [FREUND, Y. and Schapire. R.E. (1997). A decision-theoretic generalization of on-line learning and an application …

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What size net gives valid generalization … Solving multiclass learning problems via error-correcting output codes. J.Artif.Intell.Res.,2,263286 [8] H. Drucker and C. Cortes. 1996. Boosting decision trees, Adv. Neural Inform. Process.

Linear programming boosting via column generation. Machine Learning, 46(1- 3):225-254, 2002. [2] Yoav. Freund and Robert E. Schapire. A decision theoretic generalization of online learning and application to boosting. Computer …

Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing …

Boosting for Fast Face Recognition Guo-Dong Guo and Hong-Jiang Zhang … Freund and R. E. Schapire, A decision-theoretic generalization of online learning and an application to boosting. J. Comp. & Sys. Sci., 55(1):119-139, …

Top-down algorithms such as C4.5 and CART for constructing decision trees are known to perform boosting, with the procedure of choosing classification rules at …

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This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts … Learning Theory (COLT ’90), 1990. [4] Y. Freund, R. Schapire, A Decision-Theoretic Generalization of on-Line Learning and …

Application of Boosting Classification and Regression to Modeling the Relationships Between Trace Elements and Diseases

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Boosting refers to this general problem of producing a very accurate prediction rule by combining rough and moderately inaccurate rules-of-thumb. — A decision-theoretic generalization of on-line learning and an application to …

Dec 04, 2013  · Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical …

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