Prediction and discovery : AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning: Prediction and Discovery, June 25-29, 2006, Snowbird, Utah
著者
書誌事項
Prediction and discovery : AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning: Prediction and Discovery, June 25-29, 2006, Snowbird, Utah
（Contemporary mathematics, 443）
American Mathematical Society, c2007
- タイトル別名
-
Statistical learning and data mining
大学図書館所蔵 件 / 全38件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references
内容説明・目次
内容説明
These proceedings feature some of the latest important results about machine learning based on methods originated in Computer Science and Statistics. In addition to papers discussing theoretical analysis of the performance of procedures for classification and prediction, the papers in this book cover novel versions of Support Vector Machines (SVM), Principal Component methods, Lasso prediction models, and Boosting and Clustering. Also included are applications such as multi-level spatial models for diagnosis of eye disease, hyperclique methods for identifying protein interactions, robust SVM models for detection of fraudulent banking transactions, etc. This book should be of interest to researchers who want to learn about the various new directions that the field is taking, to graduate students who want to find a useful and exciting topic for their research or learn the latest techniques for conducting comparative studies, and to engineers and scientists who want to see examples of how to modify the basic high-dimensional methods to apply to real world applications with special conditions and constraints.
目次
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.
「Nielsen BookData」 より