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

Table of Contents (Back)

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 Small 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
14.1.4.4 Domain Adaptation

14.1.4.4.1 Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption
14.1.4.4.2 Adversarial Networks for Transfer Learning, Domain Adaption

14.1.4.5 Zero-Shot Learning
14.1.4.6 One Shot Learning, Few Shot Learning
14.1.4.7 Data Augmentation, Generative Network, Convolutional Network
14.1.4.8 Metric Learning
14.1.4.9 Multi-View Learning, Transfer from Other View
14.1.4.10 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 Voting for Combinations, Classifiers
14.1.5.2.2 Bagging, Combinations, Classifiers
14.1.5.2.3 Hierarchical Combination, Multi-Stage Classifiers
14.1.5.2.4 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 Density Based Clustering
14.2.2.2 Binary Clustering, Two Class Classification
14.2.2.3 One Class Clustering, One Class Classification
14.2.2.4 High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification

14.2.2.4.1 Hyperspectral Data, Neural Networks for Classification
14.2.2.4.2 Spectral-Spatial Classification, Hyperspectral Data
14.2.2.4.3 Hyperspectral Data, Endmember Extraction
14.2.2.4.4 Hyperspectral Data, Dimensionality Reduction, Band Selection
14.2.2.4.5 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

14.2.8.1.1 Hyperspectral Unmixing

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
14.2.14.2 Archetypal Analysis

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

14.2.19 Extreme Learning Machine, ELM

14.3 Robust Techniques, Robust Classification

14.3.1 Outlier Detection and Analysis, Robust Analysis
14.3.2 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, RNN
14.5.7.3.2 Residual Neural Networks, ResNet

14.5.7.4 Neural Networks for Shapes and Complex Features
14.5.7.5 Convolutional Neural Networks for Image Descriptions

14.5.7.5.1 Graph Convolutional Neural Networks
14.5.7.5.2 Convolutional Neural Networks, Design, Implementation Issues
14.5.7.5.3 Pooling in Convolutional Neural Networks Implementations
14.5.7.5.4 Efficient Implementations Convolutional Neural Networks
14.5.7.5.5 Neural Net Pruning
14.5.7.5.6 Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks
14.5.7.5.7 Convolutional Neural Networks for Object Detection and Segmentation
14.5.7.5.8 Deep Learning, Deep Nets
14.5.7.5.9 Siamese Networks

14.5.7.6 Neural Networks Applied to Specific Problems
14.5.7.7 Adversarial Networks, Adversarial Inputs, Generative Adversarial

14.5.7.7.1 Countering Adversarial Attacks, Defense, Robustness

14.5.8 Bayesian Learning, Bayes Network, Bayesian Networks
14.5.9 Genetic Algorithms, Genetic Programming
14.5.10 Learning -- Conference Listing

Keywords found in this Chapter: 2-D Grammars. ATR. AdaBoost. Adversarial Networks. Anomaly Detection. Archetypal Analysis. Augmentation. Award, ACCV. Award, ACCV Best Paper. Award, CVPR. Award, CVPR, HM. Award, CVPR, Student. Award, ICPR. Award, JVCI. Award, King Sun Fu. Award, Longuet-Higgins. Award, Marr Prize. Award, PRL Most Cited. Award, Pattern Recognition, Best Paper. Award, Pattern Recognition, Honorable Mention. Bagging. Band Selection. Bayes. Bayes Classifier. Bayes Nets. Bayesian Clustering. Bayesian Learning. Binary Clustering. Boosting. C-Means Clustering. CNN. Cartography. Cascade Classifier. Classifer Combinations. Classification. Classifier Combinations. Classifier Ensembles. Clustering. Clustering, Hierarchical. Clustering, Iterative. Clusters Detection. Code Convolutional Networks. Code, Classification. Code, Clustering. Code, Convolutional Networks. Code, Convolutional Neural Nets. Code, Domain Adaption. Code, Face Recognition. Code, Generative Adversarial Network. Code, Generative Adversarial Networks. Code, HEIV. Code, Learning. Code, Matlab. Code, Metric Learning. Code, Modes. Code, Neural Netowrks. Code, Neural Networks. Code, Object Detection. Code, Pattern Recognition. Code, Search. Code, Support Vector Machines. Combination. Comparisons. Connectionist. Context. Convolutional Neural Networks. Data Augmentation. Data Fusion. Dataset, Hyperspectral. Dataset, Learning. Dataset, Products. Dataset, Vehicles. Decision Fusion. Decision Tree. Deep Nets. Density Based. Dictionary Learning. Dimensionality. Dimensionality Reduction. Discriminant Analysis. Discrimination Rule. Distance Measures. Domain Adaptation. Domain Adaption. ECOC. ELM. Efficient Implementation. Endmember Extraction. Ensemble Classification. Error Estimation. Evaluation. Evaluation, Classifiers. Evaluation, Learning. Evaluation, Neural Networks. Evaluation, Samples. Explainable. Extreme Learning Machines. Face Detection. Face Recognition. Feature Description. Feature Extraction. Feature Selection. Few-Shot Learning. Fisher Discriminant. Forgetting. Fuzzy Clustering. GAN. Generative Networks. Genetic Algorithms. Genetic Programming. Grammar, Two Dimensional. Grammars. Graph Convolutional Neural Networks. HEIV. Handwritten Characters. Hierarchical Classification. Hyperspectral. ISODATA Clustering. Imbalanced Data. Invariants. Iterative Classification. K-Means. Kernel Covariance. Kernel Learning. Knowledge-Based Vision. LDA. Learning. Learning Models. Least Median. Linear Discriminant Analysis. Localization. MAT. MKL. Matching, Clustering. Metric Learning. Minimal Spanning Tree. Mixed Pixels. Mixture Models. Model Based Vision. Multi-Label Classification. Multi-Task Learning. Multi-View Learning. Multiple Instance Learning. Multiple Kernal. Nearest Neighbor. Neighborhood Graph. Neural Networks. Noise Removal. Nonparametric Clustering. Number of Clusters. OCR. Object Descriptions. Object Detection. One Class. One-Shot Learning. Optimal Path Forest. Outliers. PCA. Parallel Algorithms. Parzen. Pattern Recognition. Perceptrons. Ph.D.. Pooling. Pose Estimation. Principal Component Analysis. Principal Components. Probabilistic Causal Model. Probability Density Estimation. Pruning. RNN. ROC Analysis. Random Forest. Recognition. Recurrent Neural Networks. Regularization. Relaxation. Remote Sensing. Residual Neural Networks. Retrieval. Road Following. Robust Technique. SVM. Sample Size. Segmentation. Self-Organizing Map. Semi-Supervised. Semi-Supervised Clustering. Semi-Supervised Learning. Siamese Networks. Similarity. Single Shot Learning. Small Sample Size. Spanning Tree. Spectral Clustering. Spectral-Spatial. Statistical Learning. Structural Texture. Subspace Clustering. Support Vector Machines. Survay, Pattern Classification. Survey, Bee Colony. Survey, Clustering. Survey, Convolutional Networks. Survey, Convolutional Neural Networks. Survey, Decision Tree. Survey, Deep Learning. Survey, Deep Nets. Survey, Deep Neural Networks. Survey, Dimensionality. Survey, Discrminiant Analysis. Survey, Ensemble Clustering. Survey, Evaluation. Survey, Evolutionary Clustering. Survey, Grammars. Survey, Hyperspectral Imaging. Survey, Hyperspectral Targets. Survey, Imbalanced Data. Survey, Learning. Survey, Metric Learning. Survey, Neural Networks. Survey, Object Detection. Survey, Pattern Recognition. Survey, ROC Analysis. Survey, Random Forests. Survey, SVM. Survey, Support Vector Machines. Survey, Transfer Learning. Syntactic. Syntactic Recognition. Syntatic Descriptions. Training Set. Transfer Learning. Tree Classifiers. Two Class. Unbalanced Data. Unmixing. Unsupervised. Voting. Zero-Shot Learning. pLSA.
Last update:Jun 29, 2020 at 10:24:28