14 Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms Table of Contents (Back)

Keyword list at end

• 14.1 Pattern Recognition Issues
  • 14.1.1 Pattern Recognition, General and Survey Articles
  • 14.1.2 Pattern Recognition Systems
  • 14.1.3 Feature Selection in Pattern Recognition or Clustering
    • 14.1.3.1 Number of Features, Dimensionality, Dimensionality Reduction
    • 14.1.3.2 Discriminant Analysis
  • 14.1.4 Training Set Size, Analysis, Selection
    • 14.1.4.1 Sample Sizes Issues, Data analysis, Training Sets
  • 14.1.5 Classifier, Performance Evaluation, Errors, Comparisons
    • 14.1.5.1 Error Estimation, Classification Accuracy
    • 14.1.5.2 Multiple Classifiers, Combining Classifiers, Combinations
      • 14.1.5.2.1 Hierarchical Combination, Multi-Stage Classifiers
      • 14.1.5.2.2 Classifier Combination, Evaluation, Overview, Appliction Specific
    • 14.1.5.3 Decision Fusion
  • 14.1.6 King Sun Fu Pattern Recognition Papers
• 14.2 Pattern Recognition and Clustering Techniques
  • 14.2.1 Pattern Recognition, General Issues
  • 14.2.2 Clustering, Classification, General
    • 14.2.2.1 High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification
    • 14.2.2.2 Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly
  • 14.2.3 Unsupervised Clustering, Classification
  • 14.2.4 Iterative, Hierarchical Clustering Techniques
  • 14.2.5 Distance Measures, Criteria for Clustering
  • 14.2.6 Mixture Models, Mixed Pixels
    • 14.2.6.1 Mixed Pixels, Subpixel Classification
  • 14.2.7 Detecting Clusters and Number of Clusters
  • 14.2.8 Nearest Neighbor Classification
    • 14.2.8.1 Fast Nearest Neighbor Techniques
  • 14.2.9 Linear Separable Classification
  • 14.2.10 Fuzzy Clustering, Fuzzy Classification Techniques
    • 14.2.10.1 Fuzzy Clustering, Cluster Validity Tests
    • 14.2.10.2 Fuzzy Clustering, Overview, Summary, Comparisons
  • 14.2.11 Fisher, Parzen, and Other Clustering Measures and Decompositions
    • 14.2.11.1 Bayesian Clustering, Bayes Classifier
  • 14.2.12 K-Means Clustering
    • 14.2.12.1 ISODATA Clustering
  • 14.2.13 Nonparametric Clustering
  • 14.2.14 Clustering Applications
  • 14.2.15 Support Vector Machines, SVM
• 14.3 Robust Techniques, Robust Classification
  • 14.3.1 Boosting, AdaBoost Technique
• 14.4 Syntactic Methods for Image Analysis
  • 14.4.1 Two Dimensional Grammars, Syntactic, Graph Grammars
  • 14.4.2 Syntactic Methods for Region Segmentation
  • 14.4.3 Decision Trees
  • 14.4.4 Context and Structure for Classification
• 14.5 Learning in Computer Vision
  • 14.5.1 Learning, General Surveys, Overviews
  • 14.5.2 Evaluation and Analysis of Learning Techniques
  • 14.5.3 Learning Object Descriptions, Object Recognition
    • 14.5.3.1 Learning Model Descriptions
  • 14.5.4 Statistical Learning, Clustering, Learning Feature Values
  • 14.5.5 Neural Networks
    • 14.5.5.1 Neural Networks: General, Survey, Special Issues
    • 14.5.5.2 Neural Networks Combinations and Evaluations
    • 14.5.5.3 Neural Networks for Classification and Pattern Recognition
    • 14.5.5.4 Neural Networks for Shapes and Complex Features
    • 14.5.5.5 Neural Networks Applied to Specific Problems
  • 14.5.6 Bayesian Learning
  • 14.5.7 Genetic Algorithms, Genetic Programming
  • 14.5.8 Learning -- Conference Listing