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Prediction and Discovery

Joseph Stephen Verducci, Xiaotong Shen, and John Lafferty, editors
Publisher: 
American Mathematical Society
Publication Date: 
2007
Number of Pages: 
226
Format: 
Paperback
Series: 
Contemporary Mathematics 443
Price: 
69.00
ISBN: 
978-0-8218-4195-2
Category: 
Proceedings
We do not plan to review this book.
  • J. S. Verducci and X. Shen -- Introduction
  • J. Wang, X. Shen, and W. Pan -- On transductive support vector machines
  • X. Deng, M. Yuan, and A. Sudjianto -- A note on robust kernel principal component analysis
  • Y. Liu, H. H. Zhang, C. Park, and J. Ahn -- The $L_q$ support vector machine
  • Y. Wu and Y. Liu -- On multicategory truncated-hinge-loss support vector machines
  • A. B. Owen -- A robust hybrid of lasso and ridge regression
  • Y. Kim, Y. Kim, and J. Kim -- A gradient descent algorithm for LASSO
  • B. Li and P. K. Goel -- Additive regression trees and smoothing splines-predictive modeling and interpretation in data mining
  • E. P. Fokoué -- Estimation of atom prevalence for optimal prediction
  • C. Rudin, R. E. Schapire, and I. Daubechies -- Precise statements of convergence for AdaBoost and arc-gv
  • K. Marsolo, S. Parthasarathy, M. Twa, and M. Bullimore -- Ensemble-learning by model-based spatial averaging
  • H. Zou, J. Zhu, S. Rosset, and T. Hastie -- Automatic bias correction methods in semi-supervised learning
  • S. Wang and J. Zhu -- Variable selection for model-based high-dimensional clustering
  • W. Pan and X. Shen -- Semi-supervised learning via constraints
  • M. Steinbach, P - N. Tan, H. Xiong, and V. Kumar -- Objective measures for association pattern analysis

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