Benediktsson et al., 1993 - Google Patents
Parallel consensual neural networksBenediktsson et al., 1993
- Document ID
- 2863650669493823283
- Author
- Benediktsson J
- Sveinsson J
- Ersoy O
- Swain P
- Publication year
- Publication venue
- IEEE International Conference on Neural Networks
External Links
Snippet
A neural network architecture is proposed and applied in classification of remote sensing/geographic data from multiple sources. The architecture is called the parallel consensual neural network, and its relation to hierarchical and ensemble neural networks is …
- 230000001537 neural 0 title abstract description 57
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Pasquet et al. | Photometric redshifts from SDSS images using a convolutional neural network | |
Bishop | Neural networks: a pattern recognition perspective | |
Benediktsson et al. | Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data | |
US5402522A (en) | Dynamically stable associative learning neural system | |
Errington et al. | Application of artificial neural networks to chromosome classification | |
CN111814607B (en) | Deep learning model suitable for small sample hyperspectral image classification | |
WO1990014631A1 (en) | Dynamically stable associative learning neural system | |
Daqi et al. | Classification methodologies of multilayer perceptrons with sigmoid activation functions | |
Liu et al. | $\beta $-Dropout: A Unified Dropout | |
Benediktsson et al. | Parallel consensual neural networks | |
Duch et al. | Initialization and optimization of multilayered perceptrons | |
Mali et al. | Recognizing long grammatical sequences using recurrent networks augmented with an external differentiable stack | |
Davis et al. | Predicting direction shifts on Canadian–US exchange rates with artificial neural networks | |
EP0935212B9 (en) | N-Tuple or ram based neural network classification system and method | |
Allam Jr et al. | Paying attention to astronomical transients: introducing the time-series transformer for photometric classification | |
Gallicchio et al. | Deep tree echo state networks | |
Cho et al. | Parallel, self-organizing, hierarchical neural networks with competitive learning and safe rejection schemes | |
Bacauskiene et al. | Selecting salient features for classification based on neural network committees | |
Benediktsson et al. | Consensual neural networks | |
Hui et al. | Robust deflated canonical correlation analysis via feature factoring for multi-view image classification | |
Alshrief et al. | Ensemble machine learning model for classification of handwritten digit recognition | |
Deco et al. | Statistical-ensemble theory of redundancy reduction and the duality between unsupervised and supervised neural learning | |
Ersoy et al. | Parallel, self-organizing, hierarchical neural networks. II | |
Domeniconi et al. | Adaptive metric nearest neighbor classification | |
Gromov et al. | A Language and Its Dimensions: Intrinsic Dimensions of Language Fractal Structures |