You are previewing Machine Learning.
O'Reilly logo
Machine Learning

Book Description

Statistics, psychology, and computer science are major influences in machine learning research. This exciting interdisciplinary science is a crucial component in many cutting-edge systems and business processes. Innovations in machine learning stand to change financial markets and uncover mysteries inherent in human learning. Machine Learning: Concepts, Methodologies, Tools, and Applications offers a wide-ranging selection of key research in a complex field of study. This multi-volume set will cover both broad concepts and specific applications. Chapters will discuss topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editor-in-Chief
    2. Associate Editors
    3. Editorial Advisory Board
  5. Preface
  6. Section 1: Fundamental Concepts and Theories
    1. Chapter 101: A Comparison of Human and Computer Information Processing
      1. Introduction
      2. Background
      3. Computer vs. Human Information Processing
      4. Summary
      5. Future Trends
      6. Conclusion
      7. References
      8. Key Terms and Definitions
    2. Chapter 102: Machine Learning
      1. Introduction
      2. Background
      3. What is Machine Learning?
      4. Machine Learning Tasks
      5. Machine Learning Algorithms
      6. Current Trends in Machine Learning
      7. Future Trends
      8. Conclusion
      9. Acknowledgment
      10. References
      11. Key Terms and Definitions
      12. Endnotes
    3. Chapter 103: Machine Learning Through Data Mining
      1. INTRODUCTION
      2. BACKGROUND
      3. RECENT DEVELOPMENTS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS and Definitions
    4. Chapter 104: Calibration of Machine Learning Models
      1. ABSTRACT
      2. INTRODUCTION
      3. CALIBRATION EVALUATION MEASURES
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
      8. Endnote
    5. Chapter 105: Classification of Web Pages Using Machine Learning Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE SURVEY
      4. PROBLEM DEFINITION
      5. MACHINE LEARNING
      6. WEB PAGE STRUCTURE
      7. WEB PAGE REPRESENTATION
      8. STEPS IN WEB PAGE CLASSIFICATION
      9. MACHINE LEARNING TECHNIQUES
      10. RECENT TRENDS IN CLASSIFYING THE WEB PAGES
      11. PRIVACY PRESERVING DATA MINING
      12. CONCLUSION
      13. REFERENCES
      14. Key Terms and Definitions
    6. Chapter 106: 3D Modelling and Artificial Intelligence
      1. Abstract
      2. Introduction
      3. Background
      4. Some links between 3D modelling and AI techniques in Engineering
      5. Future trends
      6. Conclusion
      7. Acknowledgment
      8. References
    7. Chapter 107: An Overview of Knowledge Translation
      1. INTRODUCTION
      2. BACKGROUND
      3. KNOWLEDGE TRANSLATION CHARACTERISTICS
      4. KNOWLEDGE TRANSLATION STRATEGIES
      5. FUTURE TRENDS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS and Definitions
    8. Chapter 108: Adaptive Technology and Its Applications
      1. INTRODUCTION
      2. MAIN FOCUS OF THIS article
      3. BACKGROUND
      4. A SIMPLE ILLUSTRATIVE EXAMPLE
      5. FUTURE TRENDS
      6. Author Note
      7. REFERENCES
      8. Key TERMS and Definitions
    9. Chapter 109: Adaptive Algorithms for Intelligent Geometric Computing
      1. Introduction
      2. Background
      3. Adaptive Geometric Computing
      4. Future Trends
      5. Conclusion
      6. References
      7. Key Terms and Definitions
    10. Chapter 110: Different Roles and Definitions of Spatial Data Fusion
      1. Abstract
      2. INTRODUCTION
      3. TERMINOLOGY
      4. TECHNOLOGIES WITHIN SPATIAL DATA FUSION
      5. CHALLENGES IN SPATIAL DATA FUSION
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS and Definitions
  7. Section 2: Development and Design Methodologies
    1. Chapter 201: Machine Learning as a Commonsense Reasoning Process
      1. INTRODUCTION
      2. BACKGROUND
      3. TOWARDS AN INTERACTIVE MODEL OF COMMONSENSE REASONING
      4. FUTURE TRENDS
      5. CONCLUSION
      6. References
      7. KEY TERMS and Definitions
    2. Chapter 202: Motivated Learning for Computational Intelligence
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND FOR MOTIVATED LEARNING
      4. NEED FOR MOTIVATED LEARNING
      5. MOTIVATED LEARNING METHOD
      6. Simulation experiments
      7. Case I
      8. Case II
      9. Comparing Motivated Learning and Reinforcement Learning
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. Key Terms and Definitions
    3. Chapter 203: Designing a Computational Model of Learning
      1. Abstract
      2. INTRODUCTION
      3. Initial Understandings
      4. Characteristics of the Learner
      5. Nature of Knowledge
      6. Knowledge Acquisition
      7. Understanding and Labels
      8. Hierarchy, Temporality, and Agency
      9. Ultimate Knowledge: How to Learn
      10. Applying the Knowledge Framework
      11. Community = Environment
      12. Feedback and Assessment
      13. CONCLUSION
      14. REFERENCES
      15. KEY TERMS and Definitions
    4. Chapter 204: Intelligent MAS in System Engineering and Robotics
      1. INTRODUCTION
      2. BACKGROUND
      3. MAS, AI AND SYSTEM ENGINEERING
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. key TERMS and Definitions
    5. Chapter 205: Information Hiding by Machine Learning
      1. Abstract
      2. Introduction
      3. Backgrounds and Related Works
      4. Methodology
      5. Experiments
      6. Discussion
      7. Conclusion and Future Work
      8. References
    6. Chapter 206: Rule Engines and Agent-Based Systems
      1. INTRODUCTION
      2. BACKGROUND
      3. INTEGRATION OF RULES AND AGENTS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. Key TERMS and Definitions
    7. Chapter 207: Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. APPROXIMATE DYNAMIC PROGRAMMING
      5. APPLICATION TO ECONOMIC SYSTEMS
      6. FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
      8. Additional Reading
    8. Chapter 208: A Bayesian Based Machine Learning Application to Task Analysis
      1. INTRODUCTION
      2. BACKGROUND
      3. MAIN FOCUS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS and Definitions
      8. endnote
    9. Chapter 209: Combining Classifiers and Learning Mixture-of-Experts
      1. INTRODUCTION
      2. BACKGROUND
      3. A GENERAL ARCHITECTURE, TWO TASKS, AND THREE INGREDIENTS
      4. -COMBINATION
      5. α-INTEGRATION
      6. FUTURE TRENDS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    10. Chapter 210: Designing Unsupervised Hierarchical Fuzzy Logic Systems
      1. INTRODUCTION
      2. THE GENETIC FUZZY RULE GENERATOR ARCHITECTURE
      3. VARIABLE SELECTION AND RULE BASE DECOMPOSITION
      4. ISSUES IN RULE BASE IDENTIFICATION
      5. FUTURE TRENDS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. key TERMS and Definitions
    11. Chapter 211: A Self-Organizing Neural Network to Approach Novelty Detection
      1. Abstract
      2. Introduction
      3. Background
      4. Self-Organizing Novelty Detection Neural Network Architecture
      5. Learning Behavior of SONDE
      6. First Experimental Results
      7. Time Series Modeling and Novelty Level Measurement Using SONDE
      8. Simulation with Shannon Entropy as Novelty Metric
      9. An Experiment Based on Measured Data
      10. Conclusion
      11. References
      12. Endnote
    12. Chapter 212: Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence
      1. Abstract
      2. Introduction
      3. Literature Review
      4. A Synthetic Concept of Causality Between Business Variables
      5. An Empirical Validation Approach for Causal Strategy Models
      6. Approximation of Unknown Causal Functions
      7. Experimental Results and Proof of Concept
      8. Conclusion
      9. References
      10. Endnotes
    13. Chapter 213: Designing Light Weight Intrusion Detection Systems
      1. Abstract
      2. INTRODUCTION TO INTRUSION DETECTION SYSTEMS
      3. INTRUSION DETECTION METHODS
      4. TYPES OF INTRUSION DETECTION SYSTEMS
      5. DATA MINING APPROACHES TOWARD INTRUSION DETECTION
      6. THE DATA MINING PROCESS OF BUILDING INTRUSION DETECTION MODELS
      7. FEATURE SELECTION AND CLASSIFICATION
      8. EXPERIMENTAL RESULTS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS and Definitions
    14. Chapter 214: A Multi-Agent Machine Learning Framework for Intelligent Energy Demand Management
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM DESCRIPTION
      4. The Learning Method for Optimal Cap Selection
      5. EXPERIMENTS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    15. Chapter 215: Modelling Gene Regulatory Networks Using Computational Intelligence Techniques
      1. ABSTRACT
      2. 1. Introduction
      3. 2. GRN Modelling Problem
      4. 3. Computational Modelling of GRN
      5. 4. An Illustrative Example Model
      6. 5. Conclusion
      7. References
      8. Key Terms and Definitions
  8. Section 3: Tools and Technologies
    1. Chapter 301: Application of Machine Learning Techniques to Predict Software Reliability
      1. Abstract
      2. Introduction
      3. Literature Survey
      4. Overview of the Techniques Applied
      5. Experimental Design
      6. Results and Discussions
      7. Conclusion
      8. Acknowledgment
      9. References
    2. Chapter 302: Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models
      1. Abstract
      2. 1. INTRODUCTION
      3. 2. ROLE OF ARTIFICIAL IMMUNE SYSTEM IN SOFTWARE ENGINEERING DOMAIN: MOTIVATION AND EXAMPLES
      4. 3. AIRS ALGORITHM FOR SOFTWARE FAULT PREDICTION
      5. 4. CASE STUDY: APPLICATION OF AIRS FOR FAULT PREDICTION
      6. 5. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    3. Chapter 303: A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems
      1. Abstract
      2. Introduction
      3. SYSTEM MODEL AND ASSUMPTIONS
      4. THE OVERALL APPROACH
      5. DETECTION
      6. LOCATION AND RECOVERY
      7. EXPERIMENTAL FRAMEWORK AND RESULTS
      8. RELATED WORK
      9. CONCLUSION
      10. REFERENCES
    4. Chapter 304: Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks
      1. ABSTRACT
      2. INTRODUCTION
      3. CLASSIFICATION TASKS WITH UNBALANCED DATASETS
      4. Techniques and Algorithms for Coping with Unbalanced Datasets
      5. CASE STUDIES
      6. CONCLUSION
      7. REFERENCES
      8. Key Terms and Definitions
    5. Chapter 305: Hybrid Meta-Heuristics Based System for Dynamic Scheduling
      1. INTRODUCTION
      2. BACKGROUND
      3. Hybrid Meta-heuristics based SCHEDULING SYSTEM
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. key TERMS and Definitions
    6. Chapter 306: Differential Learning Expert System in Data Management
      1. INTRODUCTION
      2. BACKGROUND
      3. KNOWLEDGE EXTRACTION FROM DIFFERENTIALLY FED NEURAL NETWORKS
      4. NEED OF DIFFERENTIAL FEEDBACK
      5. SYSTEM DESCRIPTION
      6. PROPOSED ARCHITECTURE
      7. DATA PROCESSING
      8. RULE EXTRACTION
      9. SCHEDULING DECISION
      10. FUTURE TRENDS
      11. CONCLUSION
      12. REFERENCES
      13. KEY TERMS and Definitions
    7. Chapter 307: Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Hybrid Intelligent Diagnosis Approach
      5. Prototype Design and Experimental Results
      6. FUTURE RESEARCH DIRECTIONS
      7. Conclusion
      8. REFERENCES
      9. ADDITIONAL READING
      10. ENDNOTES
    8. Chapter 308: Machine Learning Approach to Search Query Classification
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODOLOGY
      5. DISCUSSION
      6. CONCLUSION AND FUTURE RESEARCH
      7. REFERENCES
      8. Key Terms and Definitions
    9. Chapter 309: Machine Learning in Morphological Segmentation
      1. Abstract
      2. Introduction
      3. Background
      4. Machine Learning in Morphological Segmentation of Microscopic Images
      5. Future Trends
      6. Conclusion
      7. Acknowledgment
      8. References
      9. Key Terms and Definitions
    10. Chapter 310: Machine Learning Techniques for Network Intrusion Detection
      1. Abstract
      2. Introduction
      3. Related Works
      4. Artificial Neural Network
      5. Ensemble Learning
      6. Boosted Modified Probabilistic Neural Network (BMPNN)
      7. 6. Application to Network Intrusion Detection
      8. 6.1. Intrusion Detection Data
      9. 7. Conclusion and Future Research
      10. References
    11. Chapter 311: A Machine Learning Based Meta-Scheduler for Multi-Core Processors
      1. Abstract
      2. Introduction
      3. Overview of the Meta-Scheduler
      4. Metric to Characterize L2 Cache Behaviour
      5. Model Building
      6. Model Tree
      7. Prediction Accuracy of Model
      8. Transferability of Model
      9. Consideration of Multi-Core Processor Topology
      10. Policy Framework of Meta-Scheduler
      11. Consideration of CPU Affinity
      12. Test Bed for Performance Evaluation
      13. Results
      14. Related Works
      15. Conclusion and Future Work
      16. References
    12. Chapter 312: Automatic Semantic Annotation Using Machine Learning
      1. Abstract
      2. INTRODUCTION
      3. METHOLOGIES
      4. SEMANTIC ANNOTATION SYSTEMS
      5. CREAM
      6. KNOWITALL
      7. TEXTRUNNER
      8. KIM
      9. MUSE
      10. DIPRE
      11. C-PANKOW
      12. APPLICATIONS
      13. FUTURE RESEARCH DIRECTIONS
      14. CONCLUSION
      15. ACKNOWLEDGMENT
      16. REFERENCES
      17. ADDITIONAL READING
    13. Chapter 313: Electricity Load Forecasting Using Machine Learning Techniques
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. SVM-SOM: A New Local Model for Electricity Load Forecasting
      5. Maximum Electricity Load Forecasting: Case Study
      6. Experimental Results
      7. FUTURE RESEARCH DIRECTIONS
      8. Conclusion
      9. REFERENCES
      10. Key Terms and Definitions
    14. Chapter 314: Non-Topical Classification of Query Logs Using Background Knowledge
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODOLOGY
      5. DISCUSSION
      6. CONCLUSION AND FUTURE RESEARCH
      7. REFERENCES
      8. Key Terms and Definitions
    15. Chapter 315: Prediction of Compound-protein Interactions with Machine Learning Methods
      1. Abstract
      2. Introduction
      3. Background
      4. Formalism of the compound-protein interaction prediction by supervised bipartite graph inference
      5. Binary classification approach
      6. Dimension reduction approach with regression models
      7. Dimension reduction approach with distance learning
      8. Experiment
      9. FUTURE RESEARCH DIRECTION
      10. CONCLUSION
      11. REFERENCES
    16. Chapter 316: Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services
      1. Abstract
      2. Introduction
      3. Methodology
      4. Experiments
      5. Discussions
      6. Conclusion and Future Work
      7. Acknowledgment
      8. REFERENCES
    17. Chapter 317: Adaptive Ensemble Multi-Agent Based Intrusion Detection Model
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. THE PROPOSED ENSEMBLE FRAMEWORK
      5. 4. EXPERIMENTAL DATASET
      6. 5. IMPLEMENTATION AND EXPERIMENTAL RESULTS
      7. 6. CONCLUSION AND FUTURE WORK
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. Endnote
    18. Chapter 318: Class Prediction in Test Sets with Shifted Distributions
      1. INTRODUCTION
      2. BACKGROUND
      3. A NEW ALGORITHM FOR CONSTRUCTING CLASSIFIERS WHEN TRAINING AND TEST SETS HAVE DIFFERENT DISTRIBUTIONS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. Key TERMS and Definitions
    19. Chapter 319: Bankruptcy Prediction by Supervised Machine Learning Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. EXPERIMENTAL SETUP
      5. EXPERIMENTAL RESULTS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. Key Terms and Definitions
    20. Chapter 320: Bankruptcy Prediction through Artificial Intelligence
      1. Introduction
      2. Background
      3. AI for Bankruptcy Prediction
      4. Financial Application
      5. Dataset
      6. Future Trends
      7. Conclusion
      8. References
      9. Key Terms and Definitions
      10. Endnotes
      11. Appendix
  9. Section 4: Utilization and Application
    1. Chapter 401: Machine Learning and Data Mining in Bioinformatics
      1. INTRODUCTION
      2. BACKGROUND
      3. BIOLOGICAL DATA ANALYSIS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS and Definitions
    2. Chapter 402: Machine Learning for Biometrics
      1. ABSTRACT
      2. Introduction
      3. A General Look at Biometric Systems
      4. Learning and Matching the Biometric Template
      5. DYNAMIC INFORMATION
      6. Multiple Biometrics and Information Fusion
      7. Evaluating a Biometrics System
      8. Conclusion
      9. ACKNOWLEDGMENT
      10. References
      11. KEY TERMS AND DEFINITIONS
      12. endnotes
    3. Chapter 403: Pattern Discovery from Biological Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BIOLOGICAL BACKGROUND
      4. TYPES OF CANCERS
      5. MACHINE LEARNING BACKGROUND
      6. Association Learning
      7. MACHINE LEARNING TECHNIQUES FOR CANCER PATIENT CLASSIFICATION
      8. GENE CLUSTERING
      9. EXPERIMENTAL OUTCOME
      10. LIMITATION OF THE EXISTING TECHNIQUES
      11. FUTURE AIM
      12. DISCUSSIONS
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    4. Chapter 404: Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. Texture and Shape Characteristics of Masses
      5. Machine learning techniques
      6. Support Vector Machine
      7. Computer-Aided Detection and Diagnosis of Breast Cancer
      8. Future Research Directions
      9. Conclusion
      10. References
    5. Chapter 405: Ensemble of Neural Networks for Automated Cell Phenotype Image Classification
      1. Abstract
      2. Introduction
      3. Overview of automated methods FOR cell phenotype image classification
      4. Descriptors for cell phenotype images
      5. MACHine learning CLASSification approaches
      6. A new approach based on local and global descriptors
      7. PERFORMANCE EVALUATION
      8. Future Research Directions
      9. Conclusion
      10. ACKNOWLEDGMENT
      11. References
    6. Chapter 406: Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images
      1. Abstract
      2. Introduction
      3. Background
      4. Microarray segmentation methods
      5. Classification-based Application
      6. Future Trends
      7. Conclusion
      8. References
      9. Key Terms and Definitions
    7. Chapter 407: Machine Learning for Automated Polyp Detection in Computed Tomography Colonography
      1. Abstract
      2. Introduction
      3. Automated Polyp Detection in CTC
      4. Materials and Methods
      5. Feature Extraction of Candidate Polyps
      6. Computational requirements for feature extraction
      7. Classification of candidate polyp surfaces
      8. Future Research Directions
      9. Conclusion
      10. Acknowledgment
      11. References
    8. Chapter 408: Machine Learning for Brain Image Segmentation
      1. Abstract
      2. Introduction
      3. Background
      4. Learning for Segmentation
      5. Future Research Directions
      6. Conclusion
      7. Acknowledgment
      8. References
    9. Chapter 409: Machine Learning for Clinical Data Processing
      1. Abstract
      2. Introduction
      3. Summary
      4. Acknowledgment
      5. References
      6. KEY TERMS AND DEFINITIONS
    10. Chapter 410: A Simulation of Temporally Variant Agent Interaction via Passive Inquiry
      1. Abstract
      2. INTRODUCTION
      3. THE SIMULATIONS
      4. RULE VARIATIONS
      5. Conclusion
      6. References
      7. Additional Reading
      8. Endnotes
    11. Chapter 411: A Simulation of Temporally Variant Agent Interaction via Belief Promulgation
      1. Abstract
      2. INTRODUCTION
      3. MESSAGE DRIVEN COMMUNICATION
      4. The Application Framework
      5. Hardware/Software Details
      6. Conclusion
      7. References
      8. Additional Reading
      9. Endnotes
    12. Chapter 412: Application of Uncertain Variables to Knowledge-Based Resource Distribution
      1. Abstract
      2. Introduction
      3. Knowledge Representation and Resource Distribution Problems
      4. Resource Distribution for Parallel and Cascade Operations
      5. Conclusion
      6. References
    13. Chapter 413: Applying Commonsense Reasoning to Place Identification
      1. Abstract
      2. 1. Introduction
      3. 2. Main Components
      4. 3. Applying Cyc to the Whereabouts Diary
      5. 4. Demonstration
      6. 5. Experiments
      7. 6. Related Work
      8. 7. Conclusions and Future Works
      9. 8. Acknowledgment
      10. 9. References
    14. Chapter 414: Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems
      1. Abstract
      2. Motivation
      3. Related Work
      4. Discussion and Conclusion
      5. References
      6. Key Terms and Definitions
    15. Chapter 415: Visual Semantic Analysis to Support Semi-Automatic Modeling of Semantic Service Descriptions
      1. Abstract
      2. INTRODUCTION
      3. Services and Semantic Analysis
      4. Semantic Service Description Approaches and Semantic Analysis Process Models
      5. A Generic Process Model for the Visual Semantic Analysis
      6. A conceptual framework for the Visual Semantic Analysis
      7. Application Scenarios
      8. Conclusion and future work
      9. Acknowledgment
      10. REFERENCES
      11. Endnotes
    16. Chapter 416: Learning Algorithms for RBF Functions and Subspace Based Functions
      1. ABSTRACT
      2. BACKGROUND
      3. THE EM ALGORITHM AND BEYOND: ALTERNATIVE ME VERSUS ENRBF
      4. TWO STAGE IMPLEMENTATION VERSUS AUTOMATIC MODEL SELECTION
      5. BYY HARMONY LEARNING: FUNDEMENTALS
      6. BYY HARMONY LEARNING: CHARACTERISTICS AND IMPLEMENTATIONS
      7. SUBSPACE BASED FUNCTIONS AND CASCADED EXTENSIONS
      8. FUTURE TRENDS
      9. CONCLUSION
      10. Acknowledgment
      11. References
      12. Key Terms and their Definitions
    17. Chapter 417: Annotating Images by Mining Image Search
      1. Abstract
      2. INTRODUCTION
      3. Related Works
      4. Theory Behind: The Motivation
      5. The Modeless Annotation Approach of Mining Search Results
      6. Evaluations
      7. Discussions
      8. Conclusion
      9. Acknowledgment
      10. References
      11. endnotes
    18. Chapter 418: Machine Learning for Visual Navigation of Unmanned Ground Vehicles
      1. ABSTRACT
      2. INTRODUCTION
      3. THE TRAINING DATA PREPROCESSING
      4. MACHINE LEARNING ALGORITHMS
      5. EXPERIMENTS AND DISCUSSION
      6. CONCLUSION
      7. References
      8. KEY TERMS AND DEFINITIONS
    19. Chapter 419: Empirical Evaluation of Ensemble Learning for Credit Scoring
      1. Abstract
      2. Introduction
      3. Related Work
      4. Ensemble Learning for Credit Scoring
      5. Experimental Design
      6. Results and Analyses
      7. Conclusion and Future Directions
      8. ACKNOWLEDGMENT
      9. Reference
      10. Key Terms and Definitions
    20. Chapter 420: A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments
      1. Abstract
      2. 1. INTRODUCTION
      3. 2. THE HUMAN MEMORY-BASED COMPUTATIONAL MODEL
      4. 3. THE ERRORS REMEDIATION PRINCIPLE
      5. 4. EXPERIMENTAL VALIDATION
      6. 5. THE AUTHORING TOOL
      7. 6. DISCUSSION
      8. 7. CONCLUSION
      9. Acknowledgment
      10. References
  10. Section 5: Organizational and Social Implications
    1. Chapter 501: Conservation of Information (COI)
      1. Abstract
      2. Introduction
      3. Background
      4. Main Focus of the Chapter
      5. New Case Studies: Telemedicine and eHealth
      6. Introduction to Three Case Studies
      7. Case Study Project #1: Southeast Public Health District (SEPHD)
      8. Case Study Project #2: East Georgia Health Cooperative (EGHC)
      9. Case Study Project #3: Island Health Care (IHC) – The Healthcare Alternatives (THA) Group
      10. Models of Telemedicine and eHealth
      11. Future Trends
      12. Summary
      13. References
      14. Key Terms and Definitions
      15. Endnote
    2. Chapter 502: Computational Intelligence for Functional Testing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SEARCH BASED UML-BASED TESTING
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. Acknowledgment
      8. REFERENCES
      9. ADDITIONAL READING
      10. Endnotes
    3. Chapter 503: An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
      1. Abstract
      2. Introduction
      3. Background
      4. Immune approach to the multi-player pursuit-evasion problem
      5. Conclusion
      6. Future research directions
      7. References
      8. Additional reading
      9. Key terms and Definitions
    4. Chapter 504: Artificial Intelligence in Software Engineering
      1. ABSTRACT
      2. INTRODUCTION
      3. Use of AI in Planning and Project Effort Estimation
      4. REQUIREMENTS ENGINEERING AND SOFTWARE DESIGN
      5. TESTING
      6. CONCLUSION
      7. REFERENCES
    5. Chapter 505: FOL Learning for Knowledge Discovery in Documents
      1. ABSTRACT
      2. Introduction
      3. THE DOCUMENT PROCESSING DOMAIN
      4. RELATED WORK
      5. THE DOMINUS FRAMEWORK
      6. FIRST-ORDER LOGIC PRELIMINARIES
      7. SIMILARITY FRAMEWORK
      8. INCREMENTAL LEARNING FRAMEWORK
      9. EXPERIMENTS
      10. Conclusion
      11. References
      12. Key Terms and Their Definitions
    6. Chapter 506: Computer-Based Learning Environments with Emotional Agents
      1. Abstract
      2. Introduction
      3. Emotion and computing
      4. Computational models of emotion
      5. Motivation and emotion in instructional design
      6. Computer-based learning environments
      7. Emotional aspect of interaction mediated by computers
      8. The socio-emotional climate in computer-based learning environments
      9. Agent paradigms, architectures and emotion
      10. Emotional agents in education
      11. The socio-emotional agent’s functions in instructional design
      12. Conclusion and future trends
      13. References
      14. Key Terms and Definitions
      15. Endnotes
    7. Chapter 507: Emotional Memory and Adaptive Personalities
      1. Abstract
      2. Introduction
      3. Background
      4. Emotional Long Term Memory for Configuration and Personality
      5. Future Trends
      6. Conclusion
      7. References
      8. Key Terms and Definitions
    8. Chapter 508: Hybrid Emotionally Aware Mediated Multiagency
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. INTRODUCTION TO THE HYBRID ARCHITECTURE
      5. The Agent and Its Environment
      6. CONCLUSION
      7. FUTURE WORK
      8. REFERENCES
    9. Chapter 509: Automatic Detection of Emotion in Music
      1. Abstract
      2. Introduction
      3. Section 1. Music and emotions: emotion in music and emotions from music
      4. Section 2. Music Information Retrieval: Building automatic detectors of music emotions
      5. Section 3. From Music Information Retrieval to personalized emotion-based music assistants
      6. Future Trends
      7. Conclusion
      8. Acknowledgment
      9. References
      10. Key Terms and Definitions
      11. Endnotes
    10. Chapter 510: Improving Automated Planning with Machine Learning
      1. abstract
      2. Introduction
      3. background
      4. learning ISSUES WITHIN automated Planning
      5. Learning Planning Control knowledge
      6. Learning Planning ACTION MODELS
      7. Future trends
      8. Conclusion
      9. References
      10. Key Terms ANd Definitions
      11. endnotes
    11. Chapter 511: Diagnostic Support Systems and Computational Intelligence
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. DIAGNOSIS OF FOCAL LIVER LESIONS FROM NON-ENHANCED CT IMAGES
      5. FUTURE TRENDS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. References
      9. Key Terms and Definitions
    12. Chapter 512: Computer Aided Knowledge Discovery in Biomedicine
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. KNOWLEDGE DISCOVERY IN BIOMEDICAL APPLICATIONS
      5. FUTURE TRENDS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS and Definitions
    13. Chapter 513: Computerised Decision Support for Women's Health Informatics
      1. Abstract
      2. Introduction
      3. Clinical Decision Making
      4. Decision Analysis
      5. Artificial Intelligence and Decision Support
      6. Knowledge Discovery from Databases (KDD)
      7. Expert Systems and Knowledge Based Approaches
      8. Systems in Action
      9. Reminder Systems
      10. Discussion
      11. Future Research Directions
      12. References
      13. Additional Reading
    14. Chapter 514: Translation of Biomedical Terms by Inferring Rewriting Rules
      1. Abstract
      2. Introduction
      3. Scientific context
      4. Translation technique
      5. Translation Experiments
      6. Application to a CLIR Experiment
      7. Concluding remarks and perspectives
      8. References
    15. Chapter 515: Machine Learning in Personalized Anemia Treatment
      1. ABSTRACT
      2. Introduction
      3. CONCLUSION
      4. ACKNOWLEDGMENT
      5. REFERENCES
      6. Key TERMS AND DEFINITIONS
    16. Chapter 516: An Intelligent Algorithm for Home Sleep Apnoea Test Device
      1. ABSTRACT
      2. Introduction
      3. Background
      4. Discussion
      5. Conclusion
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    17. Chapter 517: Application of Machine Leaning in Drug Discovery and Development
      1. Abstract
      2. INTRODUCTION
      3. COMMONLY USED MACHINE LEARNING TECHNIQUES
      4. APPLICATION OF MACHINE LEARNING IN DRUG DISCOVERY AND DEVELOPMENT
      5. CHALLENGES AND FUTURE DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
    18. Chapter 518: Learning and Prediction of Complex Molecular Structure-Property Relationships
      1. Abstract
      2. INTRODUCTION
      3. THE MODERN DRUG DISCOVERY PIPELINE: INTRODUCTION AND ISSUES
      4. COMPUTATIONAL CHALLENGES IN STRUCTURE-ACTIVITY MODELING
      5. MODELING STRUCTURE-PROPERTY RELATIONSHIPS: PROPOSED APPROACH
      6. EXPERIMENTAL INVESTIGATION AND CASE STUDY
      7. CONCLUSION AND DISCUSSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
    19. Chapter 519: Artificial Intelligence and Rubble-Mound Breakwater Stability
      1. INTRODUCTION
      2. BACKGROUND
      3. PHYSICAL MODEL AND ANN MODEL
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. Key TERMS and Definitions
    20. Chapter 520: Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DATASETS
      5. PROCESSING METHODS
      6. RESULTS AND DISCUSSION
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
  11. Section 6: Managerial Impact
    1. Chapter 601: Introducing AI and IA into a Non Computer Science Graduate Programme
      1. ABSTRACT
      2. INTRODUCTION
      3. The Context: A Master’s ProgramMe in Technology, Innovation and Entrepreneurship
      4. Design of the Course
      5. Teaching Administration, Coordination and Coherence
      6. Motivation: Capturing the Students’ Interest
      7. Delivery
      8. Coursework Assignment
      9. Discussion
      10. Conclusion
      11. References
    2. Chapter 602: Distributed Intelligence for Constructing Economic Models
      1. Abstract
      2. Introduction
      3. System Architecture
      4. Experimental Results
      5. Conclusion
      6. Acknowledgment
      7. References
    3. Chapter 603: A Computational Intelligence Approach to Supply Chain Demand Forecasting
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. ARTIFICIAL NEURAL NETWORKS
      5. THE OLMAM ALGORITHM
      6. SUPPORT VECTOR MACHINES
      7. INFORMATION SOURCES
      8. RESULTS
      9. CONCLUSION
      10. References
      11. Endnotes
    4. Chapter 604: Artificial Intelligence Applied to Natural Resources Management
      1. Abstract
      2. Introduction
      3. Related Works
      4. Cellular Automata
      5. Multi-Agent-Based Simulation
      6. Future Directions and Conclusions
      7. References
    5. Chapter 605: Balance Modelling and Implementation of Flow Balance for Application in Air Traffic Management
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED RESEARCHES
      4. THE BALANCE METHODOLOGY
      5. FLOW BALANCE MODULE
      6. MODELLING
      7. IMPLEMENTATION
      8. TESTS AND RESULTS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
    6. Chapter 606: Computational Intelligence for Information Technology Project Management
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. Current CI Applications FOR IT Project Management
      5. CONCLUSION
      6. REFERENCES
      7. ADDITIONAL READING
    7. Chapter 607: Cost-Sensitive Learning in Medicine
      1. Abstract
      2. Introduction
      3. Cost-Sensitive Classification
      4. Current Cost-Sensitive Approaches
      5. Evaluation of Classifiers
      6. ROC Graphs
      7. Conclusion
      8. Future Trends
      9. References
      10. Key Terms and Definitions
      11. Endnote
    8. Chapter 608: Data Warehousing and Decision Support in Mobile Wireless Patient Monitoring
      1. Abstract
      2. Introduction
      3. Medical Rationale
      4. Technical Background of Wireless Patient Monitoring
      5. Clinical DSS for Wireless Patient Monitoring
      6. Future Directions and Conclusion
      7. REFERENCES
      8. KEY TERMS and Definitions
    9. Chapter 609: Forecasting Supply Chain Demand Using Machine Learning Algorithms
      1. Abstract
      2. Introduction
      3. Background
      4. Research Methodology
      5. Experimental Design
      6. Results
      7. Conclusion and Discussion
      8. References
      9. Appendix
    10. Chapter 610: Machine Learning and Financial Investing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHOD
      5. RESULTS AND ANALYSIS
      6. DESIGN OF THE FINANCIAL INVESTING SYSTEM
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  12. Section 7: Critical Issues
    1. Chapter 701: Problems for Structure Learning Aggregation and Computational Complexity
      1. Abstract
      2. Introduction
      3. Theory: Learning from Aggregations
      4. Experimental Results
      5. Complexity without Conditional Independence
      6. Conclusion
      7. References
    2. Chapter 702: Granular Computing and Human-Centricity in Computational Intelligence
      1. Abstract
      2. Introduction
      3. Design of Information Granules
      4. The Principle of Justifiable Granularity
      5. Information Granules as Results of Clustering
      6. Design of Information Granules with Knowledge Hints
      7. Knowledge Reconciliation: Several Mechanisms of Collaboration
      8. The Development of Granular Models of Type-2
      9. Conclusion
      10. References
    3. Chapter 703: Walking the Information Overload Tightrope
      1. Abstract
      2. Information and Information Overload
      3. Levels of Information Overload
      4. Information Theory, Information Overload, and Social Life
      5. Sub-Kinds and Consequences of Information Overload: Current and Future Trends
      6. How Can—How Should—We Deal with Information Overload’s Current and Likely Future Trends?
      7. Conclusion
      8. REFERENCES
      9. KEY TERMS and Definitions
      10. ENDNOTES
    4. Chapter 704: Moral Emotions for Autonomous Agents
      1. Abstract
      2. 1. Introduction
      3. 2. Looking for a control system of moral reasoning
      4. 3. Emotions and moral understanding
      5. 4. Intrinsic motivation and emotions
      6. 5. Open questions, troubling prospects
      7. Acknowledgment
      8. References
    5. Chapter 705: Artificial Moral Agency in Technoethics
      1. Abstract
      2. Introduction
      3. Background
      4. Implications
      5. Future Trends
      6. Conclusion
      7. References
      8. Key Terms and Definitions
      9. Endnotes
    6. Chapter 706: Emotions, Diffusive Emotional Control and the Motivational Problem for Autonomous Cognitive Systems
      1. Abstract
      2. Introduction
      3. Motivations
      4. Neuromodulators
      5. Cognitive Systems
      6. Survival Variables
      7. Autonomous Dynamics
      8. Associative Thinking
      9. Input Recognition
      10. Emotional Control
      11. Conclusion
      12. References
      13. Key terms and Definitions
    7. Chapter 707: Embodying Cognition
      1. Abstract
      2. 1. From Symbols to Bodies
      3. 2. The Quest for Enactive AI
      4. 3. Enactive AI? Morphology to the Rescue!
      5. 4. Previous Research: Simulations and Lego NXT Robots
      6. 5. Morphological Computing as Cognition: XOR Robots
      7. 6. Some Philosophical Conclusions
      8. Acknowledgment
      9. References
      10. Key Terms and Definitions
      11. Endnotes
    8. Chapter 708: A Cognitive Computational Knowledge Representation Theory
      1. Abstract
      2. Introduction
      3. The AURELLIO Knowledge Representation Approach
      4. The Authoring Tool
      5. Practical Validations
      6. Discussion
      7. Conclusion
      8. Acknowledgment
      9. References
      10. Endnotes
    9. Chapter 709: Noble Ape's Cognitive Simulation
      1. Abstract
      2. Artificial Life, Noble Ape and Agar
      3. Agar Information Transfer and Cognition
      4. Tuning an Instrument in a Non-Linear Key
      5. Applied Use of the Cognitive Simulation
      6. Future Directions
      7. New Kinds of Thinking
      8. References
    10. Chapter 710: Ethology-Based Approximate Adaptive Learning
      1. Abstract
      2. Introduction
      3. Background
      4. Near Sets and Adaptive Learning
      5. Adaptive Learning
      6. Conclusion
      7. Future Research Directions
      8. Acknowledgment
      9. References
      10. Key Terms and Definitions
      11. Endnote
  13. Section 8: Emerging Trends
    1. Chapter 801: Learning with Partial Supervision
      1. INTRODUCTION
      2. BACKGROUND
      3. MAIN FOCUS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS and Definitions
    2. Chapter 802: Brain-Like Processing and Classification of Chemical Data
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. PROCESSING AND CLASSIFICATION OF CHEMICAL DATA INSPIRED BY INSECT OLFACTION
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    3. Chapter 803: Modern Approaches to Software Engineering in the Compositional Era
      1. Abstract
      2. Introduction
      3. Ontology Guided Search and Integration
      4. Domain Specific Inference
      5. Conclusion
      6. References
    4. Chapter 804: Pattern Discovery as Event Association
      1. INTRODUCTION
      2. BACKGROUND
      3. MAIN FOCUS
      4. FUTURE TRENDS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS and Definitions
    5. Chapter 805: A Survey of Optimized Learning Pathway Planning and Assessment Paper Generation with Swarm Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. SWARM INTELLIGENCE
      4. SWARM INTELLIGENCE FOR CONTENT PLANNING and ASSESSMENT PAPER GENERATION
      5. CONCLUSION
      6. REFERENCES
      7. ADDITIONAL READING
      8. KEY TERMS AND DEFINITIONS
    6. Chapter 806: A Next Generation Technology Victim Location and Low Level Assessment Framework for Occupational Disasters Caused by Natural Hazards
      1. Abstract
      2. Introduction
      3. Service Functionality And Implementation
      4. Conclusion
      5. References
    7. Chapter 807: Facial Expression Analysis by Machine Learning
      1. Abstract
      2. Introduction
      3. Background
      4. Facial Expression Systems
      5. Feature Extraction
      6. Feature Selection
      7. Classification
      8. Future Trends and Conclusion
      9. References
      10. Key Terms and Definitions
    8. Chapter 808: Discovering Semantics from Visual Information
      1. Abstract
      2. 1. INTRODUCTION
      3. 2. VISUAL INFORMATION REPRESENTATION
      4. 3. ANNOTATION METHODOLOGIES
      5. 4. DATASETS FOR ANNOTATION
      6. 5. SEMANTIC RELEVANCE AMONG CONCEPTS IN VISUAL DOMAIN
      7. 6. SUMMARY
      8. ACKNOWLEDGMENT
      9. References
      10. Endnotes
    9. Chapter 809: From Biomedical Image Analysis to Biomedical Image Understanding Using Machine Learning
      1. Abstract
      2. Introduction
      3. Biomedical Images
      4. Machine Learning
      5. Biomedical image analysis
      6. From Image Analysis to Image Understanding
      7. Visual attention models
      8. Image Collections Analysis
      9. Semantic Multimodality
      10. Image-User Interaction
      11. Conclusion
      12. References
    10. Chapter 810: The Application of Machine Learning Technique for Malaria Diagnosis
      1. Abstract
      2. 1. Introduction
      3. 2. Literature Review
      4. 3. Materials /Method
      5. 4. Operating System and Programming Platform
      6. 5. Implementation
      7. 6. Results
      8. 7. Discussion
      9. 8. Conclusion
      10. References
    11. Chapter 811: Application of Machine Learning Techniques for Railway Health Monitoring
      1. Abstract
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. REGRESSION ALGORITHMS
      5. ENERGY-EFFICIENT DATA ACQUISITION MODEL
      6. MONITORING VERTICAL ACCELERATION OF RAILWAY WAGONS
      7. DISCUSSIONS
      8. REFERENCES
    12. Chapter 812: Explorative Data Analysis of In-Vitro Neuronal Network Behavior Based on an Unsupervised Learning Approach
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. Data analysis of in-vitro neuronal network activity
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONs
    13. Chapter 813: Modeling the Ecological Footprint of Nations via Evolutionary Computation and Machine Learning Models
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
      4. METHODOLOGY
      5. RESULTS
      6. SELF-ORGANIZING MAPS CLUSTERING
      7. HYPOTHESES TESTING VIA BAYESIAN REGRESSION
      8. CONVERGENCE DIAGNOSTICS
      9. IMPLICATIONS AND LIMITATIONS
      10. REFERENCES
      11. Key Terms and Definitions
    14. Chapter 814: Adaptive Interaction for Mass Customisation
      1. Abstract
      2. Introduction
      3. Background
      4. Conclusion and Future Work
      5. References
    15. Chapter 815: Dependency Parsing
      1. INTRODUCTION
      2. DEPENDENCY GRAMMAR
      3. DEPENDENCY TREEBANKS
      4. DEPENDENCY PARSING
      5. FUTURE TRENDS
      6. CONCLUSION
      7. REFERENCES
      8. Key TERMS and Definitions
      9. Endnote