Prediction and discovery : AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning: Prediction and Discovery, June 25-29, 2006, Snowbird, Utah
Statistical Learning Prediction, Joseph S. Verducci, Xiaotong T. Shen, John Lafferty · American Mathematical Society eBooks · 2007
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.