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Soft Computing Applications in Business

  • Editors
  • Bhanu Prasad

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 230)

Table of contents

  1. Front Matter
  2. Chris Matthews, Esther Scheurmann
    Pages 1-18
  3. Haizhou Li, Hong Yan
    Pages 19-34
  4. Rodney D. Nielsen, Wayne Ward, James H. Martin
    Pages 201-230
  5. Vijay Kumar Mago, Bhanu Prasad, Ajay Bhatia, Anjali Mago
    Pages 231-242
  6. M. L. Borrajo, E. S. Corchado, M. A. Pellicer, J. M. Corchado
    Pages 243-260
  7. Ugo Galassi, Attilio Giordana, Lorenza Saitta
    Pages 273-292
  8. Back Matter

About this book

Introduction

Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field.

The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques.

The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody’s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting.

Keywords

Markov Markov model audit calculus clustering decision tree ensemble learning evolution fuzzy hidden Markov Model learning modeling neural network programming scheduling

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-79005-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-79004-4
  • Online ISBN 978-3-540-79005-1
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site