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 Feature Selection using Search
    • 14.1.3.2 Number of Features, Dimensionality, Dimensionality Reduction
    • 14.1.3.3 Discriminant Analysis
  • 14.1.4 Training Set Size, Analysis, Selection
    • 14.1.4.1 Sample Sizes Issues, Data analysis, Training Sets
    • 14.1.4.2 Transfer Learning from Other Classes
  • 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, Dimensionality Reduction
    • 14.2.2.3 Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly
  • 14.2.3 Unsupervised Clustering, Classification
  • 14.2.4 Semi-Supervised Clustering, Classification
  • 14.2.5 Iterative, Hierarchical Clustering Techniques
  • 14.2.6 Distance Measures, Criteria for Clustering
  • 14.2.7 Mixture Models, Mixed Pixels
    • 14.2.7.1 Mixed Pixels, Unmixing
    • 14.2.7.2 Mixed Pixels, Subpixel Classification
  • 14.2.8 Detecting Clusters and Number of Clusters
  • 14.2.9 Nearest Neighbor Classification
    • 14.2.9.1 Fast Nearest Neighbor Techniques
  • 14.2.10 Linear Separable Classification
  • 14.2.11 Fuzzy Clustering, Fuzzy Classification Techniques
    • 14.2.11.1 Fuzzy Clustering, Cluster Validity Tests
    • 14.2.11.2 Fuzzy Clustering, Overview, Summary, Comparisons
  • 14.2.12 Fisher, Parzen, and Other Clustering Measures and Decompositions
    • 14.2.12.1 Bayesian Clustering, Bayes Classifier
  • 14.2.13 K-Means Clustering
    • 14.2.13.1 ISODATA Clustering
  • 14.2.14 Nonparametric Clustering
  • 14.2.15 Clustering Applications
  • 14.2.16 Support Vector Machines, SVM
    • 14.2.16.1 Support Vector Machines, SVM, Incremental, Multi-Step
    • 14.2.16.2 Support Vector Machines, SVM, Applied to Recognition
    • 14.2.16.3 Support Vector Machines, SVM, Feature Selection
    • 14.2.16.4 Support Vector Machines, SVM, One-Class Classification
    • 14.2.16.5 Support Vector Machines, SVM, Surveys, Reviews, General
• 14.3 Robust Techniques, Robust Classification
  • 14.3.1 Boosting, AdaBoost Technique
• 14.4 Structural, 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 Non-Vision Learning Issues
  • 14.5.2 Learning, General Surveys, Overviews
  • 14.5.3 Evaluation and Analysis of Learning Techniques
  • 14.5.4 Learning Object Descriptions, Object Recognition
    • 14.5.4.1 Learning Model Descriptions
  • 14.5.5 Statistical Learning, Clustering, Learning Feature Values
  • 14.5.6 Neural Networks
    • 14.5.6.1 Neural Networks: General, Survey, Special Issues
    • 14.5.6.2 Neural Networks Combinations and Evaluations
    • 14.5.6.3 Neural Networks for Classification and Pattern Recognition
    • 14.5.6.4 Neural Networks for Shapes and Complex Features
    • 14.5.6.5 Neural Networks Applied to Specific Problems
  • 14.5.7 Bayesian Learning, Bayes Network, Bayesian Networks
  • 14.5.8 Genetic Algorithms, Genetic Programming
  • 14.5.9 Learning -- Conference Listing