Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps

Martinetz, Thomas

doi:10.1007/978-1-4471-2063-6_104

Cited by 256Neural Networks and ApplicationsTopological and Geometric Data AnalysisMachine Learning and ELM

Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps

Martinetz, Thomas·1993·ICANN ’93

DOI 10.1007/978-1-4471-2063-6_104

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TOPIC relies on the neural gas network to preserve the feature space topology using Hebbian learning.
“TOpologyPreserving knowledge InCrementer [71] relies on the NG network to preserve the feature space topology using a Hebbian learning [42].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [42]

The Organization of Behavior
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825 · see citing works
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529 · see citing works
The self-organizing map
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414 · see citing works
Sequence regularity and geometry of orientation columns in the monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1974) · The Journal of Comparative Neurology · doi:10.1002/cne.901580304 · Cited by 1,027 · see citing works
How patterned neural connections can be set up by self-organization
Willshaw, D. J., & von der Malsburg, C. (1976) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1976.0087 · Cited by 822 · see citing works
Multiple Representations of the Body Within the Primary Somatosensory Cortex of Primates
Kaas, J. H., Nelson, R. J., Sur, M., Lin, C.-S., & Merzenich, M. M. (1979) · Science · doi:10.1126/science.107591 · Cited by 710 · see citing works
A dimension reduction framework for understanding cortical maps
Durbin, R. M., & Mitchison, G. (1990) · Nature · doi:10.1038/343644a0 · Cited by 440 · see citing works
Computational Maps In The Brain
Knudsen, E. I., du Lac, S., & Esterly, S. D. (1987) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.10.030187.000353 · Cited by 390 · see citing works
Variants of self-organizing maps
Kangas, J. A., Kohonen, T., & Laaksonen, J. T. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80208 · Cited by 329 · see citing works
Topology-conserving maps for learning visuo-motor-coordination
Ritter, H. J., Martinetz, T., & Schulten, K. J. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90001-4 · Cited by 263 · see citing works
Self-citation
Neural Axis Representing Target Range in the Auditory Cortex of the Mustache Bat
Suga, N., & O'Neill, W. E. (1979) · Science · doi:10.1126/science.482944 · Cited by 231 · see citing works
Formation of topographic maps and columnar microstructures in nerve fields
Takeuchi, A., & Амари, Ш.-И. (1979) · Biological Cybernetics · doi:10.1007/bf00337432 · Cited by 142 · see citing works

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Growing cell structures—A self-organizing network for unsupervised and supervised learning
Fritzke, B. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90091-4 · Cited by 1,269 · see citing works
Few-Shot Class-Incremental Learning
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., & Gong, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01220 · Cited by 499 · see citing works
A self-organising network that grows when required
Marsland, S., Shapiro, J., & Nehmzow, U. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00078-3 · Cited by 391 · see citing works
Clustering: A neural network approach
Du, K.-L. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.08.007 · Cited by 326 · see citing works
An incremental network for on-line unsupervised classification and topology learning
Furao, S., & Hasegawa, O. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.04.006 · Cited by 315 · see citing works
Topology preservation in self-organizing feature maps: exact definition and measurement
Villmann, T., Der, R., Herrmann, J. M., & Martinetz, T. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.557663 · Cited by 311 · see citing works
Self-citation
Neural Network Dynamics for Path Planning and Obstacle Avoidance
Glasius, R., Komoda, A., & Gielen, S. C. A. M. (1995) · Neural Networks · doi:10.1016/0893-6080(94)e0045-m · Cited by 288 · see citing works
Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997
Samuel Kaski, A., Kangas, J. A., & Kohonen, T. (1998) · Cited by 260 · see citing works
A self-organizing network that can follow non-stationary distributions
Fritzke, B. (1997) · Lecture notes in computer science · doi:10.1007/bfb0020222 · Cited by 192 · see citing works
Fast learning with incremental RBF networks
Fritzke, B. (1994) · Neural Processing Letters · doi:10.1007/bf02312392 · Cited by 192 · see citing works
Topology-Preserving Class-Incremental Learning
Tao, X., Chang, X., Hong, X., Wei, X., & Gong, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58529-7_16 · Cited by 162 · see citing works
Lifelong learning of human actions with deep neural network self-organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.09.001 · Cited by 127 · see citing works
Online growing neural gas for anomaly detection in changing surveillance scenes
Sun, Q., Liu, H., & Harada, T. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.016 · Cited by 118 · see citing works
Self-organizing Maps
Hulle, M. M. V. (2012) · Springer eBooks · doi:10.1007/978-3-540-92910-9_19 · Cited by 114 · see citing works
Robust growing neural gas algorithm with application in cluster analysis
Qin, A. K., & Suganthan, P. N. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.06.013 · Cited by 111 · see citing works
Neural maps and topographic vector quantization
Bauer, H.-U., Herrmann, J. M., & Villmann, T. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00027-1 · Cited by 99 · see citing works
An incremental growing neural gas learns topologies
Prudent, Y., & Ennaji, A. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556026 · Cited by 90 · see citing works
Self-organizing neural integration of pose-motion features for human action recognition
Parisi, G. I., Weber, C., & Wermter, S. (2015) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2015.00003 · Cited by 79 · see citing works
Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey
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Soft Object Deformation Monitoring and Learning for Model-Based Robotic Hand Manipulation
Creţu, A.-M., Payeur, P., & Petriu, E. M. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2176115 · Cited by 63 · see citing works
A multi-agent system for web-based risk management in small and medium business
Bajo, J., Diz, M. L. B., Paz, J. F. D., Corchado, J. M., & Pellicer, M. A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.001 · Cited by 56 · see citing works
Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey
Wang, P., Zhou, Y., Luo, Q., Han, C., Niu, Y., & Lei, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.06.112 · Cited by 54 · see citing works
Model of experts for decision support in the diagnosis of leukemia patients
Corchado, J. M., Paz, J. F. D., Rodrı́guez, S., & Bajo, J. (2009) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.12.001 · Cited by 53 · see citing works
Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas
Atsalakis, A., & Papamarkos, N. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.05.004 · Cited by 52 · see citing works
Human motion assessment in real time using recurrent self-organization
Parisi, G. I., Magg, S., & Wermter, S. (2016) · IEEE RO-MAN · doi:10.1109/roman.2016.7745093 · Cited by 51 · see citing works
Hybrid neural document clustering using guided self-organization and WordNet
Hung, C., Wermter, S., & Smith, P. (2004) · IEEE Intelligent Systems · doi:10.1109/mis.2004.1274914 · Cited by 48 · see citing works
Defense Against Adversarial Attacks Using Topology Aligning Adversarial Training
Kuang, H., Liu, H., Lin, X., & Ji, R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3359820 · Cited by 46 · see citing works
Dynamic Cell Structures
Brüske, J., & Sommer, G. (1994) · neural information processing systems · Cited by 44 · see citing works
Emergence of multimodal action representations from neural network self-organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2016) · Cognitive Systems Research · doi:10.1016/j.cogsys.2016.08.002 · Cited by 41 · see citing works
A case for neuromorphic ISAs
Hashmi, A. G., Nere, A., Thomas, J. J., & Lipasti, M. H. (2011) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/1950365.1950385 · Cited by 40 · see citing works
Interaction Analysis with a Bayesian Trajectory Model
Dore, A., & Regazzoni, C. S. (2010) · IEEE Intelligent Systems · doi:10.1109/mis.2010.37 · Cited by 39 · see citing works
Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network
Yu, H., Jie Lu, B., & Zhang, G. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2947658 · Cited by 39 · see citing works
Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
Churamani, N., Barros, P. V. A., Güneş, H., & Wermter, S. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.717193 · Cited by 43 · see citing works
Introducing MONEDA
Martí, L., Garcı́a, J., Berlanga, A., & Molina, J. M. (2008) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1389095.1389230 · Cited by 35 · see citing works
A new topological clustering algorithm for interval data
Cabanès, G., Bennani, Y., Destenay, R., & Hardy, A. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.03.023 · Cited by 33 · see citing works
Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario
Cruz, F., Parisi, G. I., Twiefel, J., & Wermter, S. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759137 · Cited by 33 · see citing works
A self-organizing neural network architecture for learning human-object interactions
Mici, L., Parisi, G. I., & Wermter, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.015 · Cited by 33 · see citing works
Autonomous Growing Neural Gas for applications with time constraint: Optimal parameter estimation
García‐Rodríguez, J., Angelopoulou, A., García‐Chamizo, J. M., Ψαρρού, Α., Orts‐Escolano, S., & Morell, V. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.032 · Cited by 31 · see citing works
Emotional Bodily Expressions for Culturally Competent Robots through Long Term Human-Robot Interaction
Tuyen, N. T. V., Jeong, S., & Chong, N. Y. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593974 · Cited by 31 · see citing works
Learning the Number of Clusters in Self Organizing Map
Cabanès, G., & Bennani, Y. (2010) · InTech eBooks · doi:10.5772/9164 · Cited by 31 · see citing works
Surveillance and human–computer interaction applications of self-growing models
García‐Rodríguez, J., & García‐Chamizo, J. M. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.02.007 · Cited by 31 · see citing works
3D reconstruction of medical images from slices automatically landmarked with growing neural models
Angelopoulou, A., Psarrou, A., Garcia-Rodriguez, J., Orts-Escolano, S., Azorin-Lopez, J., & Revett, K. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.03.078 · Cited by 31 · see citing works
Learning Bodily Expression of Emotion for Social Robots Through Human Interaction
Tuyen, N. T. V., Elibol, A., & Chong, N. Y. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3005907 · Cited by 30 · see citing works
A simultaneous two-level clustering algorithm for automatic model selection
Cabanès, G., & Bennani, Y. (2007) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2007.71 · Cited by 30 · see citing works
Automatic Landmarking of 2D Medical Shapes Using the Growing Neural Gas Network
Angelopoulou, A., Ψαρρού, Α., García‐Rodríguez, J., & Revett, K. (2005) · Lecture notes in computer science · doi:10.1007/11569541_22 · Cited by 27 · see citing works
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, C. C. (1997) · NASA Technical Reports Server (NASA) · Cited by 26 · see citing works
An Adaptive Incremental Clustering Method based on the Growing Neural Gas Algorithm
Bouguelia, M.-R., Belaïd, Y., & Belaı̈d, A. (2013) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0004256600420049 · Cited by 24 · see citing works
Beta Scale Invariant Map
Quintián, H., & Corchado, E. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.01.002 · Cited by 21 · see citing works
An Online Semi-Supervised Clustering Algorithm Based on a Self-organizing Incremental Neural Network
Kamiya, Y., Ishii, T., Furao, S., & Hasegawa, O. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371105 · Cited by 20 · see citing works
Mapping the dimensionality, density and topology of data: The growing adaptive neural gas
Cselényi, Z. (2005) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2005.02.001 · Cited by 20 · see citing works
An intelligent real-time power management system with active learning prediction engine for PV grid-tied systems
Kow, K. W., Wong, Y. W., Rajkumar, R. K., & Isa, D. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.084 · Cited by 19 · see citing works
Rule Extraction From Dynamic Cell Structure Neural Network Used in a Safety Critical Application.
Darrah, M., Taylor, B. J., & Skias, S. T. (2004) · The Florida AI Research Society · Cited by 19 · see citing works
Evaluation of growing neural gas networks for selective 3D scanning
Creţu, A.-M., Petriu, E. M., & Payeur, P. (2008) · IEEE International Symposium on Robotic and Sensors Environments · doi:10.1109/rose.2008.4669190 · Cited by 18 · see citing works
Solving complex high-dimensional problems with the multi-objective neural estimation of distribution algorithm
Martí, L., Garcı́a, J., Berlanga, A., & Molina, J. M. (2009) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1569901.1569987 · Cited by 18 · see citing works
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Moser, D. M., Wittmann, S. K., Kramer, J. S., Blöcher, R., Achenbach, J., Pogoryelov, D., & Proschak, E. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500618u · Cited by 18 · see citing works
Comparison of 0.5% ropivacaine and 0.5% levobupivacaine for infraclavicular brachial plexus block.
Mageswaran, R., & Choy, Y. C. (2010) · PubMed · Cited by 18 · see citing works
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Orts‐Escolano, S., García‐Rodríguez, J., Viejo, D., Cazorla, M., & Morell, V. (2012) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2012.05.008 · Cited by 17 · see citing works
Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor
Xing, Y., Shen, F., & Zhao, J. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2416353 · Cited by 17 · see citing works
Static Gesture Recognition Combining Graph and Appearance Features
Marimpis, A. D. (2014) · INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE · doi:10.14569/ijarai.2014.030201 · Cited by 17 · see citing works
SPAWNN: A Toolkit for SPatial Analysis With Self‐Organizing Neural Networks
Hagenauer, J., & Helbich, M. (2016) · Transactions in GIS · doi:10.1111/tgis.12180 · Cited by 16 · see citing works
Cluster Analysis using Growing Neural Gas and Graph Partitioning
Costa, J. A. F., & Oliveira, R. S. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371447 · Cited by 16 · see citing works
Locally Weighted Interpolating Growing Neural Gas
Flentge, F. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.879771 · Cited by 16 · see citing works
A new incremental neural network for simultaneous clustering and classification
Hebboul, A., Hachouf, F., & Boulemnadjel, A. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.084 · Cited by 15 · see citing works
An algorithm for incremental unsupervised learning and topology representation
Shen, F. (2006) · Cited by 15 · see citing works
Scaling the Growing Neural Gas for Visual Cluster Analysis
Ventocilla, E., Martins, R. M., Paulovich, F. V., & Riveiro, M. (2021) · Big Data Research · doi:10.1016/j.bdr.2021.100254 · Cited by 14 · see citing works
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Bai, H., & Hui, S. C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121755 · Cited by 14 · see citing works
Self-organizing adaptive map: Autonomous learning of curves and surfaces from point samples
Piastra, M. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.07.007 · Cited by 14 · see citing works
Using Neural Gas Networks in Traffic Navigation
Vaščák, J. (2009) · Acta Technica Jaurinensis · Cited by 14 · see citing works
Organization of big metrology data within the Cyber-Physical Manufacturing Metrology Model (CPM3)
Sabbagh, R., Živković, S., Gawlik, B., Sreenivasan, S. V., Stothert, A., Majstorović, V., & Djurdjanović, D. (2021) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2021.10.009 · Cited by 14 · see citing works
Online incremental clustering with distance metric learning for high dimensional data
Okada, S., & Nishida, T. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033478 · Cited by 14 · see citing works
Deformable Object Segmentation and Contour Tracking in Image Sequences Using Unsupervised Networks
Creţu, A.-M., Petriu, E. M., Payeur, P., & Khalil, F. F. (2010) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2010.43 · Cited by 14 · see citing works
A Robotic Head Neuro-controller Based on Biologically-Inspired Neural Models
Asuni, G., Teti, G., Laschi, C., Guglielmelli, E., & Dario, P. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570466 · Cited by 14 · see citing works
Geometric and Bayesian models for safe navigation in dynamic environments
Laugier, C., Vasquez, D., Yguel, M., Fraichard, T., & Aycard, O. (2007) · Intelligent Service Robotics · doi:10.1007/s11370-007-0004-1 · Cited by 14 · see citing works
Few-Shot Class-Incremental LearningPreprint
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., & Gong, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10956 · Cited by 14 · see citing works
Modification of the Growing Neural Gas Algorithm for Cluster Analysis
Canales, F., & Chacón, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-76725-1_71 · Cited by 13 · see citing works
Approximate spectral clustering with eigenvector selection and self-tuned k
Alshammari, M. A., & Takatsuka, M. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.006 · Cited by 13 · see citing works
Growing Neural Networks using Soft Competitive Learning
Chaudhary, V., Ahlawat, A., & Bhatia, R. S. (2011) · International Journal of Computer Applications · doi:10.5120/2495-3372 · Cited by 13 · see citing works
A new learning algorithm for incremental self-organizing maps
Prudent, Y., & Ennaji, A. (2005) · Cited by 12 · see citing works
A Semi-Supervised Learning Algorithm for Growing Neural Gas in Face Recognition
Zaki, S. M., & Yin, H. (2008) · Journal of Mathematical Modelling and Algorithms · doi:10.1007/s10852-008-9095-8 · Cited by 12 · see citing works
Automatic Detection of Expanding H i Shells in the Canadian Galactic Plane Survey Data
Daigle, A., Joncas, G., & Parizeau, M. (2007) · The Astrophysical Journal · doi:10.1086/513501 · Cited by 12 · see citing works
Incorporating geometry knowledge into an incremental learning structure for few-shot intent recognition
Zhang, X., Jiang, M., Chen, H., Zheng, J., & Pan, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109296 · Cited by 11 · see citing works
MicroCBR: A case-based reasoning architecture for the classification of microarray data
Paz, J. F. D., Bajo, J., Vera, V., & Corchado, J. M. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.08.021 · Cited by 11 · see citing works
Neural gas sonification - growing adaptive interfaces for interacting with data
Hermann, T. M., & Ritter, H. J. (2004) · doi:10.1109/iv.2004.103 · Cited by 11 · see citing works
An incremental parallel neural network for unsupervised classification
Hebboul, A., Hacini, M., & Hachouf, F. (2011) · International Workshop on Systems, Signal Processing and their Applications · doi:10.1109/wosspa.2011.5931521 · Cited by 11 · see citing works
Object recognition in noisy RGB-D data using GNG
Rangel, J. C., Morell, V., Cazorla, M., Orts‐Escolano, S., & García‐Rodríguez, J. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0546-y · Cited by 11 · see citing works
Learning 2D Hand Shapes Using the Topology Preservation Model GNG
Angelopoulou, A., García‐Rodríguez, J., & Ψαρρού, Α. (2006) · Lecture notes in computer science · doi:10.1007/11744023_25 · Cited by 11 · see citing works
Efficient shape-based algorithms for modeling patient specific anatomy from 3D medical images: applications in virtual endoscopy and surgery
Robb, R. A., Cameron, B. M., & Aharon, S. (2002) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/sma.1997.634887 · Cited by 11 · see citing works
Double Growing Neural Gas for Disease Diagnosis
Cheng, G., & Zell, A. (2000) · Perspectives in neural computing · doi:10.1007/978-1-4471-0513-8_47 · Cited by 11 · see citing works
Extension to End-effector Position and Orientation Control of a Learning-based Neurocontroller for a Humanoid Arm
Asuni, G., Teti, G., Laschi, C., Guglielmelli, E., & Dario, P. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281904 · Cited by 10 · see citing works
A measure of disorder for the self-organizing map
Hamalainen, A. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1994.374254 · Cited by 10 · see citing works
Approximate geometry representations and sensory fusion
Szepesvári, C., & Lörincz, A. (1996) · Neurocomputing · doi:10.1016/0925-2312(95)00116-6 · Cited by 10 · see citing works
Growing neural gas network based environment selection strategy for constrained multi-objective optimization
Bai, W., Dang, Q., Wu, J., Gao, X., & Zhang, G. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121774 · Cited by 9 · see citing works
Adaptive hybrid response mechanism for dynamic multi-objective optimization and its application in multi-robot task allocation
Dong, T., Wang, P., Xue, F., Geng, Y., & Cui, Z. (2025) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2025.102123 · Cited by 9 · see citing works
Fast Image Representation with GPU-Based Growing Neural Gas
García‐Rodríguez, J., Angelopoulou, A., Morell, V., Orts‐Escolano, S., Ψαρρού, Α., & García‐Chamizo, J. M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21498-1_8 · Cited by 9 · see citing works
MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm
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Classification of Natural Language Sentences using Neural Networks
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Fast Autonomous Growing Neural Gas
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Cyrillic manual alphabet recognition in RGB and RGB-D data for sign language interpreting robotic system (SLIRS)
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Topology representing networks
Martinetz, T., & Schulten, K. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90109-0 · Cited by 879 · see citing works
Self-citation
Scanning the issue
(1994) · Neurocomputing · doi:10.1016/0925-2312(94)90017-5
Information Maximization in a Linear Manifold Topographic Map
Adibi, P., & Safabakhsh, R. (2009) · Neural Processing Letters · doi:10.1007/s11063-009-9101-9 · Cited by 1 · see citing works
A new quantitative measure of topology preservation in Kohonen's feature maps
Villmann, T., Der, R., & Martinetz, T. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1994.374251 · Cited by 33 · see citing works
Self-citation
Topology preservation in self-organizing feature maps: exact definition and measurement
Villmann, T., Der, R., Herrmann, J. M., & Martinetz, T. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.557663 · Cited by 311 · see citing works
Self-citation

Authors: Martinetz, T.
Affiliations: Siemens (Germany) (DE)
Corresponding author: Martinetz, Thomas
ORCID: Martinetz, Thomas
Venue: ICANN ’93
Publisher: Springer London
Published: 1993
Pages: 427-434
DOI: 10.1007/978-1-4471-2063-6_104
OpenAlex: W2112796159
Cited by: 256
References: 11
Citations by year: 2026: 2 · 2025: 5 · 2024: 2 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 6 · 2019: 6 · 2018: 9 · 2017: 8 · 2016: 14 · 2015: 10 · 2014: 11 · 2013: 6 · 2012: 10
FWCI: 3.16
Type: conference paper
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords: Hebbian learning, competitive learning, feature manifold, Delaunay triangulation, lateral connections, neighborhood relations, neural network architecture, manifold learning

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Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps

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