Computational intelligence has a long history of applications to business - expert systems have been used for decision support in management, neural networks and fuzzy logic have been used in process control, a variety of techniques have been used in forecasting, and data mining has become a core co
Business Applications and Computational Intelligence
โ Scribed by Kevin E. Voges, Nigel K. Ll Pope
- Publisher
- Idea Group Publishing
- Year
- 2006
- Tongue
- English
- Leaves
- 501
- Edition
- Illustrated
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Computational intelligence has a long history of applications to business - expert systems have been used for decision support in management, neural networks and fuzzy logic have been used in process control, a variety of techniques have been used in forecasting and data mining has become a core component of customer relationship management in marketing. While there is literature on this field, it is spread over many disciplines and in many different publications, making it difficult to find the pertinent information in one source. Business Applications and Computational Intelligence addresses the need for a compact overview of the diversity of applications in a number of business disciplines, and consists of chapters written by leading international researchers. Chapters cover most fields of business, including: marketing, data mining, e-commerce, production and operations, finance, decision-making, and general management. Business Applications and Computational Intelligence provides a comprehensive review of research into computational intelligence applications in business, creating a powerful guide for both newcomers and experienced researchers.
โฆ Table of Contents
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Section 1: Introduction
Chapter 1: Computational Intelligence Applications in Business
Chapter 2: Making Decisions with Data
Chapter 3: Computational Intelligence as a Platform for a Data Collection Methodology in Management Science
Section 2: Marketing Applications
Chapter 4: Heuristic Genetic Algorithm for Product Portfolio Planning
Chapter 5: Modeling Brand Choice Using Boosted and Stacked Neural Networks
Chapter 6: Applying Information Gathering Techniques in Business-to-Consumer and Web Scenarios
Chapter 7: Web Mining System for Mobile-Phone Marketing
Section 3: Production and Operations Applications
Chapter 8: Artificial Intelligence in Electricity Market Operations and Management
Chapter 9: Reinforcement Learning-Based Intelligent Agents for Improved Productivity in Container Vessel Berthing Applications
Chapter 10: Optimization Using Horizon-Scan Technique
Section 4: Data Mining Applications
Chapter 11: Visual Data Mining for Discovering Association Rules
Chapter 12: Analytical Customer Requirement Analysis Based on Data Mining
Chapter 13: Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data
Chapter 14: Support Vector Machines for Business Applications
Chapter 15: Algorithms for Data Mining
Section 5: Management Applications
Chapter 16: A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations
Chapter 17: Analyzing Strategic Stance in Public Services Management
Chapter 18: The Analytic Network Process โ Dependence and Feedback in Decision-Making
Section 6: Financial Applications
Chapter 19: Financial Classification Using an Artificial Immune System
Chapter 20: Development of Machine Learning Software for High Frequency Trading in Financial Markets
Chapter 21: Online Methods for Portfolio Selection
Section 7: Postscript
Chapter 22: Ankle Bones, Rogues, and Sexual Freedom for Women
About the Authors
Index
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