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Handbook of Research on Machine Learning Applications and Trends

Book Description

The Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques provides practical applications for solving problems and applying various techniques in automatic data extraction and setting. This Handbook of Research fills the gap between theory and practice, providing a strong reference for academicians, researchers, and practitioners.

Table of Contents

  1. Copyright
  2. Editorial Advisory Board
  3. List of Reviewers
  4. List of Contributors
  5. Foreword
  6. Preface
  7. Exploring the Unknown Nature of Data: Cluster Analysis and Applications
    1. ABSTRACT
    2. INTRODUCTION
    3. CLUSTERING ALGORITHMS
    4. NEURAL NETWORK-BASED CLUSTERING ALGORITHMS
    5. NEURAL GAS
    6. GROWING NEURAL GAS
    7. APPLICATIONS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  8. Principal Graphs and Manifolds
    1. ABSTRACT
    2. INTRODUCTION
    3. CONCLUSION
    4. REFERENCES
    5. KEY TERMS AND DEFINITIONS
  9. 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: FUNDAMENTALS
    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
  10. Nature Inspired Methods for Multi-Objective Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. BASIC CONCEPTS OF MULTI-OBJECTIVE OPTIMIZATION
    4. EVOLUTIONARY ALGORITHMS
    5. PARTICLE SWARM OPTIMIZATION
    6. ARTIFICIAL IMMUNE SYSTEMS
    7. HYBRID APPROACHES
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
    11. ENDNOTE
  11. Artificial Immune Systems for Anomaly Detection
    1. ABSTRACT
    2. INTRODUCTION
    3. NEGATIVE SELECTION
    4. EXTENSIONS OF NEGATIVE SELECTION
    5. CASE STUDY 1: ANOMALY DETECTION IN A BEARING TESTING MACHINE
    6. CASE STUDY 2: POWER QUALITY SIGNAL CLASSIFICATION
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  12. 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
  13. Classification with Incomplete Data
    1. ABSTRACT
    2. INTRODUCTION
    3. INCOMPLETE DATA CLASSIFICATION
    4. DELETION OF INCOMPLETE DATA
    5. MISSING DATA IMPUTATION METHODS
    6. MODEL BASED PROCEDURES AND EXPECTATION- MAXIMIZATION ALGORITHM
    7. EMBEDDED MACHINE LEARNING SOLUTIONS: NON IMPUTATION IS REQUIRED
    8. FUTURE TRENDS AND CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
    11. ENDNOTE
    12. ACRONYMS AND ABBREVIATIONS (IN ALPHABETICAL ORDER)
  14. Clustering and Visualization of Multivariate Time Series
    1. ABSTRACT
    2. INTRODUCTION
    3. THE ORIGINAL GENERATIVE TOPOGRAPHIC MAPPING THROUGH TIME
    4. A GAUSSIAN PROCESS FORMULATION OF GENERATIVE TOPOGRAPHIC MAPPING THROUGH TIME
    5. BAYESIAN GTM THROUGH TIME
    6. A VARIATIONAL BAYESIAN APPROACH TO GTM THROUGH TIME
    7. EXPERIMENTS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  15. Locally Recurrent Neural Networks and Their Applications
    1. ABSTRACT
    2. INTRODUCTION
    3. COMPUTATIONAL MODELS OF LOCALLY RECURRENT NEURONS
    4. IMPLEMENTATIONS OF LOCALLY RECURRENT NEURAL NETWORKS
    5. FUTURE TRENDS
    6. CONCLUSION
    7. EPILOGUE
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  16. Nonstationary Signal Analysis with Kernel Machines
    1. ABSTRACT
    2. INTRODUCTION
    3. RKHS AND KERNEL MACHINES: A BRIEF REVIEW
    4. TIME-FREQUENCY KERNEL MACHINES: THE WIGNER-VILLE DISTRIBUTION
    5. EXTENSION TO OTHER TIME-FREQUENCY DISTRIBUTIONS
    6. OPTIMAL TIME-FREQUENCY REPRESENTATION: THE KERNEL-TARGET ALIGNMENT CRITERION
    7. SIMULATION RESULTS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
    11. ENDNOTE
  17. Transfer Learning
    1. ABSTRACT
    2. INTRODUCTION
    3. TRANSFER IN INDUCTIVE LEARNING
    4. TRANSFER IN REINFORCEMENT LEARNING
    5. AVOIDING NEGATIVE TRANSFER
    6. AUTOMATICALLY MAPPING TASKS
    7. THE FUTURE OF TRANSFER LEARNING
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  18. Machine Learning in Personalized Anemia Treatment
    1. ABSTRACT
    2. INTRODUCTION
    3. CONCLUSION
    4. ACKNOWLEDGMENT
    5. REFERENCES
    6. KEY TERMS AND DEFINITIONS
  19. Deterministic Pattern Mining on Genetic Sequences
    1. ABSTRACT
    2. INTRODUCTION
    3. BASIC CONCEPTS
    4. PATTERN DISCOVERY WORKFLOW
    5. CONCLUSION
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  20. Machine Learning in Natural Language Processing
    1. ABSTRACT
    2. NATURAL LANGUAGE PROCESSING AS A CHALLENGE FOR MACHINE LEARNING
    3. THE POSITION OF MACHINE LEARNING AMONG OTHER NATURAL LANGUAGE PROCESSING TECHNOLOGIES
    4. A MACHINE LEARNING PERSPECTIVE ON NATURAL LANGUAGE PROCESSING
    5. NESTED LEARNING
    6. WORD SENSE DISAMBIGUATION
    7. DISCUSSION
    8. ABBREVIATIONS
    9. ACKNOWLEDGEMENT
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
    12. ENDNOTES
  21. Machine Learning Applications in Mega-Text Processing
    1. ABSTRACT
    2. INTRODUCTION
    3. OPINION MINING
    4. NEWS MONITORING
    5. PRIVACY PROTECTION
    6. CONCLUDING REMARKS
    7. ABBREVIATIONS
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
    11. ENDNOTES
  22. 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
  23. 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
  24. Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products
    1. ABSTRACT
    2. INTRODUCTION
    3. APPLICATIONS OF ANN IN FORECASTING
    4. THE EVOLUTIONARY MODEL
    5. AN OVERFITTING TEST
    6. EMPLOYING THE ENN MODEL IN FORECASTING
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  25. Support Vector Machine based Hybrid Classifiers and Rule Extraction thereof: Application to Bankruptcy Prediction in Banks
    1. ABSTRACT
    2. INTRODUCTION
    3. OVERVIEW OF SVM AND FUZZY RULE BASED SYSTEMS
    4. PROPOSED HYBRID APPROACH
    5. LITERATURE REVIEW OF BANKRUPTCY PREDICTION IN BANKS AND FIRMS
    6. EXPERIMENT SETUP
    7. RESULTS AND DISCUSSIONS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  26. Data Mining Experiences in Steel Industry
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS
    5. FUTURE TRENDS
    6. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND THEIR DEFINITIONS
  27. Application of Neural Networks in Animal Science
    1. ABSTRACT
    2. INTRODUCTION
    3. WEEKLY MILK PREDICTION ON DAIRY GOATS
    4. ANALYSIS OF LIVESTOCK MANAGEMENT USING THE SOM
    5. CONCLUSION
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  28. Statistical Machine Learning Approaches for Sports Video Mining Using Hidden Markov Models
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. PROBLEM FORMULATION
    5. VISUAL FEATURES
    6. HMM-BASED SEMANTIC VIDEO ANALYSIS
    7. CONCLUSION AND FUTURE WORK
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
    11. ENDNOTE
  29. A Survey of Bayesian Techniques in Computer Vision
    1. ABSTRACT
    2. INTRODUCTION
    3. INSPECTION AND CLASSIFICATION OF FRUIT AND VEGETABLES
    4. INSPECTION AND CLASSIFICATION OF GRAINS AND SEEDS
    5. WEED DETECTION AND PLANT IDENTIFICATION
    6. INSECT SEXING IN A BIO-FACTORY
    7. IMPLEMENTATION OF BAYESIAN SEGMENTATION IN REAL-TIME APPLICATIONS
    8. FUTURE TRENDS
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  30. Software Cost Estimation using Soft Computing Approaches
    1. ABSTRACT
    2. INTRODUCTION
    3. SOFT COMPUTING TECHNIQUES FOR SOFTWARE COST ESTIMATION
    4. A METHODOLOGY FOR EVALUATING SOFTWARE COST ESTIMATION APPROACHES
    5. RESULTS AND DISCUSSION
    6. CONCLUSION AND FUTURE RESEARCH
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  31. Counting the Hidden Defects in Software Documents
    1. ABSTRACT
    2. INTRODUCTION
    3. STATE OF DEFECT CONTENT ESTIMATION FOR INSPECTIONS
    4. INPUT DATA
    5. BENCHMARK DATA SET
    6. PREPROCESSING THE TRAINING DATA
    7. LEARNING THE MODEL
    8. VALIDATION
    9. CONCLUSION AND FUTURE TRENDS
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  32. 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
  33. Neural Networks for Modeling the Contact Foot-Shoe Upper
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. METHODS AND RESULTS
    5. CONCLUSION AND FUTURE WORK
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  34. Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. ARTIFICIAL NEURAL NETWORK
    5. ALGORITHM USED
    6. EXPERIMENTAL SETUP
    7. TEST FUNCTION USED
    8. ALGORITHMS PERFORMANCES COMPARISON
    9. ROBUSTNESS TESTS
    10. CONCLUSION & FUTURE WORK
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  35. 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
  36. Compilation of References
  37. About the Contributors
  38. Index