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Prediction and Discovery
 
Edited by: Joseph Stephen Verducci Ohio State University, Columbus, OH
Xiaotong Shen University of Minnesota, Minneapolis, MN
John Lafferty Carnegie Mellon University, Pittsburgh, PA
Front Cover for Prediction and Discovery
Available Formats:
Softcover ISBN: 978-0-8218-4195-2
Product Code: CONM/443
List Price: $80.00
MAA Member Price: $72.00
AMS Member Price: $64.00
Electronic ISBN: 978-0-8218-8122-4
Product Code: CONM/443.E
List Price: $75.00
MAA Member Price: $67.50
AMS Member Price: $60.00
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $120.00
MAA Member Price: $108.00
AMS Member Price: $96.00
Front Cover for Prediction and Discovery
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  • Front Cover for Prediction and Discovery
  • Back Cover for Prediction and Discovery
Prediction and Discovery
Edited by: Joseph Stephen Verducci Ohio State University, Columbus, OH
Xiaotong Shen University of Minnesota, Minneapolis, MN
John Lafferty Carnegie Mellon University, Pittsburgh, PA
Available Formats:
Softcover ISBN:  978-0-8218-4195-2
Product Code:  CONM/443
List Price: $80.00
MAA Member Price: $72.00
AMS Member Price: $64.00
Electronic ISBN:  978-0-8218-8122-4
Product Code:  CONM/443.E
List Price: $75.00
MAA Member Price: $67.50
AMS Member Price: $60.00
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $120.00
MAA Member Price: $108.00
AMS Member Price: $96.00
  • Book Details
     
     
    Contemporary Mathematics
    Volume: 4432007; 226 pp
    MSC: Primary 62;

    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
     
     
    • Articles
    • Joe Verducci and Xiaotong Shen - Introduction
    • Junhui Wang, Xiaotong Shen and Wei Pan - On transductive support vector machines [ MR 2433281 ]
    • Xinwei Deng, Ming Yuan and Agus Sudjianto - A note on robust kernel principal component analysis [ MR 2433282 ]
    • Yufeng Liu, Hao Helen Zhang, Cheolwoo Park and Jeongyoun Ahn - The $L_q$ support vector machine [ MR 2433283 ]
    • Yichao Wu and Yufeng Liu - On multicategory truncated-hinge-loss support vector machines [ MR 2433284 ]
    • Art B. Owen - A robust hybrid of lasso and ridge regression [ MR 2433285 ]
    • Yongdai Kim, Yuwon Kim and Jinseog Kim - A gradient descent algorithm for LASSO [ MR 2433286 ]
    • Bin Li and Prem K. Goel - Additive regression trees and smoothing splines—predictive modeling and interpretation in data mining [ MR 2433287 ]
    • Ernest Parfait Fokoué - Estimation of atom prevalence for optimal prediction [ MR 2433288 ]
    • Cynthia Rudin, Robert E. Schapire and Ingrid Daubechies - Precise statements of convergence for AdaBoost and arc-gv [ MR 2433289 ]
    • Keith Marsolo, Srinivasan Parthasarathy, Michael Twa and Mark Bullimore - Ensemble-learning by model-based spatial averaging [ MR 2433290 ]
    • Hui Zou, Ji Zhu, Saharon Rosset and Trevor Hastie - Automatic bias correction methods in semi-supervised learning [ MR 2433291 ]
    • Sijian Wang and Ji Zhu - Variable selection for model-based high-dimensional clustering [ MR 2433292 ]
    • Wei Pan and Xiaotong Shen - Semi-supervised learning via constraints [ MR 2433293 ]
    • Michael Steinbach, Pang-Ning Tan, Hui Xiong and Vipin Kumar - Objective measures for association pattern analysis [ MR 2433294 ]
  • Request Review Copy
  • Get Permissions
Volume: 4432007; 226 pp
MSC: Primary 62;

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.

  • Articles
  • Joe Verducci and Xiaotong Shen - Introduction
  • Junhui Wang, Xiaotong Shen and Wei Pan - On transductive support vector machines [ MR 2433281 ]
  • Xinwei Deng, Ming Yuan and Agus Sudjianto - A note on robust kernel principal component analysis [ MR 2433282 ]
  • Yufeng Liu, Hao Helen Zhang, Cheolwoo Park and Jeongyoun Ahn - The $L_q$ support vector machine [ MR 2433283 ]
  • Yichao Wu and Yufeng Liu - On multicategory truncated-hinge-loss support vector machines [ MR 2433284 ]
  • Art B. Owen - A robust hybrid of lasso and ridge regression [ MR 2433285 ]
  • Yongdai Kim, Yuwon Kim and Jinseog Kim - A gradient descent algorithm for LASSO [ MR 2433286 ]
  • Bin Li and Prem K. Goel - Additive regression trees and smoothing splines—predictive modeling and interpretation in data mining [ MR 2433287 ]
  • Ernest Parfait Fokoué - Estimation of atom prevalence for optimal prediction [ MR 2433288 ]
  • Cynthia Rudin, Robert E. Schapire and Ingrid Daubechies - Precise statements of convergence for AdaBoost and arc-gv [ MR 2433289 ]
  • Keith Marsolo, Srinivasan Parthasarathy, Michael Twa and Mark Bullimore - Ensemble-learning by model-based spatial averaging [ MR 2433290 ]
  • Hui Zou, Ji Zhu, Saharon Rosset and Trevor Hastie - Automatic bias correction methods in semi-supervised learning [ MR 2433291 ]
  • Sijian Wang and Ji Zhu - Variable selection for model-based high-dimensional clustering [ MR 2433292 ]
  • Wei Pan and Xiaotong Shen - Semi-supervised learning via constraints [ MR 2433293 ]
  • Michael Steinbach, Pang-Ning Tan, Hui Xiong and Vipin Kumar - Objective measures for association pattern analysis [ MR 2433294 ]
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