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Prediction and Discovery
Edited by: Joseph Stephen Verducci, Ohio State University, Columbus, OH, Xiaotong Shen, University of Minnesota, Minneapolis, MN, and John Lafferty, Carnegie Mellon University, Pittsburgh, PA
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Contemporary Mathematics
2007; 226 pp; softcover
Volume: 443
ISBN-10: 0-8218-4195-5
ISBN-13: 978-0-8218-4195-2
List Price: US$71
Member Price: US$56.80
Order Code: CONM/443
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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.

Readership

Research mathematicians interested in machine learning.

Table of Contents

  • 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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