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  • Introduction by J. S. Verducci and X. Shen; On transductive support vector machines by J. Wang, X. Shen, and W. Pan; A note on robust kernel principal component analysis by X. Deng, M. Yuan, and A. Sudjianto; The $L_q$ support vector machine by Y. Liu, H. H. Zhang, C. Park, and J. Ahn; On multicategory truncated-hinge-loss support vector machines by Y. Wu and Y. Liu; A robust hybrid of lasso and ridge regression by A. B. Owen; A gradient descent algorithm for LASSO by Y. Kim, Y. Kim, and J. Kim; Additive regression trees and smoothing splines-predictive modeling and interpretation in data mining by B. Li and P. K. Goel; Estimation of atom prevalence for optimal prediction by E. P. Fokoue; Precise statements of convergence for AdaBoost and arc-gv by C. Rudin, R. E. Schapire, and I. Daubechies; Ensemble-learning by model-based spatial averaging by K. Marsolo, S. Parthasarathy, M. Twa, and M. Bullimore; Automotic bias correction methods in semi-supervised learning by H. Zou, J. Zhu, S. Rosset, and T. Hastie; Variable selection for model-based high-dimensional clustering by S. Wang and J. Zhu; Semi-supervised learning via constraints by W. Pan and X. Shen; Objective measures for association pattern analysis by M. Steinbach, P - N. Tan, H. Xiong, and V. Kumar. (xsd:string)
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  • 2007 (xsd:gyear)
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  • 2007 (xsd:gyear)
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  • Englisch (EN) (xsd:string)
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  • 9780821841952 ()
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  • Prediction and discovery (xsd:string)
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  • Buch (de)
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  • book (en)
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  • GESIS-BIB (xsd:string)
  • Providence: American Mathematical Society, 2007.- 226 S., Ill. (xsd:string)
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