Prediction and Discovery

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