[go: up one dir, main page]

Wu et al., 2023 - Google Patents

An incremental learning approach for sustainable regional isolation and integration

Wu et al., 2023

View PDF
Document ID
7121638663650164318
Author
Wu Z
Yao H
Jiang H
Ma J
Publication year
Publication venue
Computers and Electrical Engineering

External Links

Snippet

Humans are capable of acquiring new knowledge on a constant basis, while integrating and optimizing old knowledge without forgetting them. This is mainly attributed to the human brain's ability of partitioned learning and memory replay. In this paper, we simulate this …
Continue reading at openreview.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/6269Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30943Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
    • G06F17/30994Browsing or visualization
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines

Similar Documents

Publication Publication Date Title
Karim et al. Deep learning-based clustering approaches for bioinformatics
Athey et al. Machine learning methods that economists should know about
Huang et al. Robust multi-view data clustering with multi-view capped-norm k-means
Huang et al. Self-weighted multi-view clustering with soft capped norm
Babbar et al. Dismec: Distributed sparse machines for extreme multi-label classification
EP3467723B1 (en) Machine learning based network model construction method and apparatus
Bousquet et al. Algorithmic stability and generalization performance
Schaaf et al. Enhancing decision tree based interpretation of deep neural networks through l1-orthogonal regularization
Fawzi et al. Dictionary learning for fast classification based on soft-thresholding
Chen et al. Diversity embedding deep matrix factorization for multi-view clustering
Jin et al. Cold-start active learning for image classification
Bourlard et al. Autoencoders reloaded
Keller et al. Learning extremal representations with deep archetypal analysis
Raschka Machine learning Q and AI: 30 essential questions and answers on machine learning and AI
Menaka et al. Chromenet: A CNN architecture with comparison of optimizers for classification of human chromosome images
CN116522143B (en) Model training methods, clustering methods, equipment and media
Fan et al. Surrogate-assisted evolutionary neural architecture search with network embedding
Zuanetti et al. Bayesian nonparametric clustering for large data sets
Sinha et al. Neural architecture search using covariance matrix adaptation evolution strategy
Tanha A multiclass boosting algorithm to labeled and unlabeled data
Xu et al. Optimizing the prototypes with a novel data weighting algorithm for enhancing the classification performance of fuzzy clustering
Perla et al. Locally-coherent multi-population mortality modelling via neural networks
Abdulrahman et al. Simplifying the algorithm selection using reduction of rankings of classification algorithms
Zhao et al. Model-based feature selection for neural networks: A mixed-integer programming approach
Du et al. Torch-choice: A PyTorch package for large-scale choice modelling with python