14 Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms

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 Unsupervised Feature Selection
    • 14.1.3.2 Feature Selection using Search and Learning
    • 14.1.3.3 Probabilistic Latent Semantic Analysis, pLSA.
    • 14.1.3.4 Error Correcting Output Codes, ECOC
    • 14.1.3.5 Number of Features, Dimensionality Reduction
      • 14.1.3.5.1 Spectral Clustering, Data Dimensionality Reduction
      • 14.1.3.5.2 PCA, Principal Component Analysis, Data Dimensionality Reduction
      • 14.1.3.5.3 Intrinsic Dimensionality
    • 14.1.3.6 Discriminant Analysis
  • 14.1.4 Training Set Size, Sample Size, Analysis, Selection
    • 14.1.4.1 Sample Sizes Issues, Data analysis, Training Sets
    • 14.1.4.2 Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data
    • 14.1.4.3 Transfer Learning from Other Classes, Domain Adaptation
    • 14.1.4.4 Zero-Shot Learning
    • 14.1.4.5 Metric Learning
    • 14.1.4.6 Multi-View Learning, Transfer from Other View
    • 14.1.4.7 Multi-Label Classification, Multilabel Classification
  • 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 Bagging, Combinations, Classifiers
      • 14.1.5.2.2 Hierarchical Combination, Multi-Stage Classifiers
      • 14.1.5.2.3 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 Binary Clustering, Two Class Classification
    • 14.2.2.2 One Class Clustering, One Class Classification
    • 14.2.2.3 High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification
    • 14.2.2.4 Spectral-Spatial Classification, Hyperspectral Data
    • 14.2.2.5 Hyperspectral Data, Endmember Extraction
    • 14.2.2.6 Hyperspectral Data, Dimensionality Reduction, Band Selection
    • 14.2.2.7 Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly
  • 14.2.3 Unsupervised Clustering, Classification
  • 14.2.4 Dictionary Learning, Classification
  • 14.2.5 Semi-Supervised Clustering, Semi-Supervised Learning, Classification
  • 14.2.6 Iterative, Hierarchical Clustering Techniques
    • 14.2.6.1 Subspace Clustering
  • 14.2.7 Distance Measures, Criteria for Clustering
  • 14.2.8 Mixture Models, Mixed Pixels
    • 14.2.8.1 Hyperspectral Mixture Models, Mixed Pixels
    • 14.2.8.2 Mixed Pixels, Unmixing
    • 14.2.8.3 Mixed Pixels, Subpixel Classification
  • 14.2.9 Detecting Clusters and Number of Clusters, Number of Classes
  • 14.2.10 Multiple Kernel Learning, MKL
  • 14.2.11 Nearest Neighbor Classification
    • 14.2.11.1 Fast Nearest Neighbor Techniques
    • 14.2.11.2 Neighborhood Graph Classification
    • 14.2.11.3 Optimal Path Forest Classification
  • 14.2.12 Linear Separable Classification
  • 14.2.13 Fuzzy Clustering, Fuzzy Classification Techniques, Fuzzy C-Means
    • 14.2.13.1 Fuzzy Clustering, Cluster Validity Tests
    • 14.2.13.2 Fuzzy Clustering, Overview, Summary, Comparisons
  • 14.2.14 Fisher, Parzen, and Other Clustering Measures and Decompositions
    • 14.2.14.1 Bayesian Clustering, Bayes Classifier
  • 14.2.15 K-Means Clustering
    • 14.2.15.1 ISODATA Clustering
  • 14.2.16 Nonparametric Clustering
  • 14.2.17 Clustering Applications
  • 14.2.18 Support Vector Machines, SVM
    • 14.2.18.1 Training Support Vector Machines, SVM Training, Learning
    • 14.2.18.2 Support Vector Machines, SVM, Incremental, Multi-Step
    • 14.2.18.3 Support Vector Machines, SVM, Applied to Recognition
    • 14.2.18.4 Support Vector Machines, SVM, Feature Selection
    • 14.2.18.5 Support Vector Machines, SVM, One-Class Classification
    • 14.2.18.6 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
    • 14.4.3.1 Random Forests Classification
  • 14.4.4 Self-Organizing Map Classification
  • 14.4.5 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
  • 14.5.5 Multiple Instance Learning
    • 14.5.5.1 Learning Model Descriptions
  • 14.5.6 Statistical Learning, Clustering, Learning Feature Values
  • 14.5.7 Neural Networks
    • 14.5.7.1 Neural Networks: General, Survey, Special Issues
    • 14.5.7.2 Neural Networks Combinations and Evaluations
    • 14.5.7.3 Neural Networks for Classification and Pattern Recognition
      • 14.5.7.3.1 Recurrent Neural Networks for Shapes and Complex Features
    • 14.5.7.4 Neural Networks for Shapes and Complex Features
    • 14.5.7.5 Convolutional Neural Networks for Image Descriptions, Deep Nets
      • 14.5.7.5.1 Convolutional Neural Networks, Design, Implementation Issues
      • 14.5.7.5.2 Convolutional Neural Networks for Object Detection and Segmentation
    • 14.5.7.6 Neural Networks Applied to Specific Problems
    • 14.5.7.7 Adversarial Networks, Adversarial Inputs
  • 14.5.8 Bayesian Learning, Bayes Network, Bayesian Networks
  • 14.5.9 Genetic Algorithms, Genetic Programming
  • 14.5.10 Learning -- Conference Listing