Contents

• List of Figures
• List of Tables
  • Acknowledgements
  
  
• Introduction
  • Summary of the thesis
  
  
• Definition of The Problem, Terms Used and Basic Issues
  • Examples of Temporal Classification
    • Tech Support
    • Recognition of signs from a sign language
  • Definition of Terms
    • Channel
    • Stream
    • Frame
    • Stream Set
  • Statement of the problem
    • Weak temporal classification
    • Strong classification
    • Pre-segmented TC
  • Assessing success
    • Assessing Accuracy of Weak TC
    • Assessing Accuracy of Strong TC
    • Comprehensibility - A subjective goal
  • The major difficulties in temporal classification
  • Conclusion
  
  
• Relationship to existing work
  • Related fields
  • Established techniques
    • Hidden Markov Models
    • Recurrent Neural Networks
    • Dynamic Time Warping
  • Recent interest from the AI community
  
  
• Metafeatures: A Novel Feature Construction Technique
  • TClass Overview
  • Tech Support revisited
  • Inspiration for metafeatures
  • Definition
  • Practical metafeatures
    • A more practical example
    • Representation
    • Extraction from a noisy signal
  • Using Metafeatures
  • Disparity Measures
    • Gain Ratio
    • Chi-Square test
  • Doing the search
  • Examples
    • With K-Means
    • With directed segmentation
  • Further refinements
    • Region membership measures
    • Bounds on variation
  • Conclusion
  
  
• Building a Temporal Learner
  • Building a practical learner employing metafeatures
  • Expanding the scope of TClass
    • Global attributes
    • Integration
  • Signal processing
    • Smoothing and filtering
  • Learners
    • Voting and Ensembles
  • Practical implementation
    • Architecture
    • Providing input
    • Implemented global extractors
    • Implemented segmenters
    • Implemented metafeatures
    • Developing metafeatures
    • Producing human-readable output in TClass
  • Temporal and spatial Analysis of TClass
  • Conclusion
  
  
• Experimental Evaluation
  • Methodology
    • Practical details
  • Artificial datasets
    • Cylinder-Bell-Funnel - A warm-up
    • TTest - An artificial dataset for temporal classification
  • Real-world datasets
    • Why these data sets?
    • Auslan
    • ECG
  • Conclusions
  
  
• Future Work
  • Work on extending TClass
    • Improving directed segmentation
    • Automatic metafeatures
    • Extending applications of metafeatures
    • Strong Temporal Classification
    • Speed and Space
    • Downsampling
    • Feature subset selection
  • Alternative approaches
    • Signal Matching
    • Approximate string-matching approaches
    • Inductive Logic Programming
    • Graph-based induction
  • Conclusions
  
  
• Conclusion
• Bibliography
• Early versions of TClass
  • Line-based segmentation
  • Per-class clustering
  
  
• About this document ...