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, Long-Tailed Data

14.1.5 Transfer Learning from Other Classes
14.1.6 Domain Adaptation

14.1.6.1 Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption
14.1.6.2 Adversarial Networks for Transfer Learning, Domain Adaption

14.1.7 Zero-Shot Learning
14.1.8 One Shot Learning, Few Shot Learning
14.1.9 Data Augmentation, Generative Network, Convolutional Network
14.1.10 Metric Learning
14.1.11 Multi-View Learning, Transfer from Other View
14.1.12 Multi-Label Classification, Multilabel Classification
14.1.13 Classifier, Performance Evaluation, Errors, Comparisons

14.1.13.1 Error Estimation, Classification Accuracy
14.1.13.2 Multiple Classifiers, Combining Classifiers, Combinations

14.1.13.2.1 Voting for Combinations, Classifiers
14.1.13.2.2 Bagging, Combinations, Classifiers
14.1.13.2.3 Hierarchical Combination, Multi-Stage Classifiers
14.1.13.2.4 Classifier Combination, Evaluation, Overview, Appliction Specific

14.1.13.3 Decision Fusion

14.1.14 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
14.2.2.4.5 Hyperspectral Data Band Selection
14.2.2.4.6 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 Continunal Learning, Incremental Learning
14.2.6.2 Subspace Clustering, Subspace Learning

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 Open Set Recongnition
14.2.11 Multiple Kernel Learning, MKL
14.2.12 Nearest Neighbor Classification

14.2.12.1 Fast Nearest Neighbor Techniques
14.2.12.2 Neighborhood Graph Classification
14.2.12.3 Optimal Path Forest Classification

14.2.13 Linear Separable Classification
14.2.14 Fuzzy Clustering, Fuzzy Classification Techniques, Fuzzy C-Means

14.2.14.1 Fuzzy Clustering, Cluster Validity Tests
14.2.14.2 Fuzzy Clustering, Overview, Summary, Comparisons

14.2.15 Fisher, Parzen, and Other Clustering Measures and Decompositions

14.2.15.1 Bayesian Clustering, Bayes Classifier
14.2.15.2 Archetypal Analysis

14.2.16 K-Means Clustering

14.2.16.1 ISODATA Clustering

14.2.17 Nonparametric Clustering
14.2.18 Clustering Applications
14.2.19 Support Vector Machines, SVM

14.2.19.1 Training Support Vector Machines, SVM Training, Learning
14.2.19.2 Support Vector Machines, SVM, Incremental, Multi-Step
14.2.19.3 Support Vector Machines, SVM, Applied to Recognition
14.2.19.4 Support Vector Machines, SVM, Feature Selection
14.2.19.5 Support Vector Machines, SVM, One-Class Classification
14.2.19.6 Support Vector Machines, SVM, Surveys, Reviews, General

14.2.20 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 LSTM: Long Short-Term Memory
14.5.8 Neural Networks

14.5.8.1 Neural Networks: General, Survey, Special Issues
14.5.8.2 Neural Networks Combinations and Evaluations
14.5.8.3 Neural Architecture, Neural Architecture Search
14.5.8.4 Neural Networks for Classification and Pattern Recognition

14.5.8.4.1 Capsule Networks
14.5.8.4.2 Recurrent Neural Networks for Shapes and Complex Features, RNN
14.5.8.4.3 Residual Neural Networks, ResNet

14.5.8.5 Neural Networks for Shapes and Complex Features
14.5.8.6 Convolutional Neural Networks for Image Descriptions

14.5.8.6.1 Graph Convolutional Neural Networks
14.5.8.6.2 Convolutional Neural Networks, Design, Implementation Issues
14.5.8.6.3 Training Issues for Convolutional Neural Networks
14.5.8.6.4 Pooling in Convolutional Neural Networks Implementations
14.5.8.6.5 Efficient Implementations Convolutional Neural Networks
14.5.8.6.6 Neural Net Pruning
14.5.8.6.7 Neural Net Compression
14.5.8.6.8 Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks
14.5.8.6.9 Convolutional Neural Networks for Object Detection and Segmentation
14.5.8.6.10 Deep Learning, Deep Nets
14.5.8.6.11 Siamese Networks

14.5.8.7 Neural Networks Applied to Specific Problems

14.5.8.7.1 Binary Neural Networks, BNN

14.5.8.8 Adversarial Networks, Adversarial Inputs, Generative Adversarial

14.5.8.8.1 Adversarial Networks for Image Synthesis
14.5.8.8.2 Countering Adversarial Attacks, Defense, Robustness
14.5.8.8.3 VAE, Variational Autoencoder

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

Keywords found in this Chapter: 2-D Grammars. ATR. Active Learning. AdaBoost. Adversarial Networks. Anomaly Detection. Archetypal Analysis. Architecture Search. Augmentation. Autoencoder. 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. Binary Networks. Boosting. C-Means Clustering. CNN. Capsule Network. Cartography. Cascade Classifier. Classifer Combinations. Classification. Classifier Combinations. Classifier Ensembles. Clustering. Clustering, Hierarchical. Clustering, Iterative. Clusters Detection. Code Convolutional Networks. Code, Adversarial Attack. Code, CNN. Code, Classification. Code, Clustering. Code, ConvNet. 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Last update:Mar 3, 2021 at 15:01:44