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On the topology distortion in self-organizing feature maps
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00201800openalex-snapshot
Self-organizing maps: stationary states, metastability and convergence rate
inferredcites →https://doi.org/10.1007/bf00317973openalex-snapshot
Analysis of a simple self-organizing process
inferredcites →https://doi.org/10.1007/bf02331338openalex-snapshot
Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps
inferredcites →https://doi.org/10.1016/0893-6080(89)90001-4openalex-snapshot
Topology-conserving maps for learning visuo-motor-coordination
inferredcites →https://doi.org/10.1049/ip-i-2.1991.0016openalex-snapshot
Digital matched-correlator automatic gain control scheme applicable to MFSK modems
inferredcites →https://doi.org/10.1049/ip-i-2.1991.0028openalex-snapshot
Dimension of the speech space
inferredcites →https://doi.org/10.1049/ip-i-2.1992.0018openalex-snapshot
Dimension and structure of the speech space
inferredcites →https://doi.org/10.1109/72.143371openalex-snapshot
Quantifying the neighborhood preservation of self-organizing feature maps
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ange.19941060610openalex-snapshot
Abbildung elektrostatischer Potentiale muscarinischer und nicotinischer Agonisten mit künstlichen neuronalen Netzen
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Mapping the Electrostatic Potential of Muscarinic and Nicotinic Agonists with Artificial Neural Networks
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Representation of molecular electrostatic potentials of biopolymer by self‐organizing feature map
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The Use of Self-organizing Neural Networks in Drug Design
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Non-Linear Dimensionality Reduction: A Comparative Performance Analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-56927-2_10openalex-snapshot
An Overview of SOM Literature
inferred← citeshttps://doi.org/10.1007/978-3-642-57030-8_13openalex-snapshot
Application of the Self-Organizing Mapping and Fuzzy Clustering to Microsatellite Data: How to Detect Genetic Structure in Brown Trout (Salmo trutta) Populations
inferred← citeshttps://doi.org/10.1007/978-3-642-97610-0_9openalex-snapshot
An Overview of SOM Literature
inferred← citeshttps://doi.org/10.1007/bf00134176openalex-snapshot
The comparison of geometric and electronic properties of molecular surfaces by neural networks: Application to the analysis of corticosteroid-binding globulin activity of steroids
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Feed-forward neural networks and topographic mappings for exploratory data analysis
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Quantitative measures for self-organizing topographic maps
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Neural networks: New tools for modelling and data analysis in science
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Data mining using rule extraction from Kohonen self-organising maps
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Virtual Libraries for Docking Methods: Guidelines for the Selection and the Preparation
inferred← citeshttps://doi.org/10.1016/b978-012213815-7/50010-8openalex-snapshot
Evaluation of Molecular Surface Properties Using a Kohonen Neural Network
inferred← citeshttps://doi.org/10.1016/j.jvlc.2011.03.004openalex-snapshot
An alternative map of the United States based on an n-dimensional model of geographic space
inferred← citeshttps://doi.org/10.1016/j.neunet.2006.05.021openalex-snapshot
Spherical self-organizing map using efficient indexed geodesic data structure
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Global collaboration through local interaction in competitive learning
inferred← citeshttps://doi.org/10.1016/s0004-3702(99)00093-4openalex-snapshot
Conformality in the self-organization network
inferred← citeshttps://doi.org/10.1016/s0079-6107(98)00026-1openalex-snapshot
Artificial neural networks for computer-based molecular design
inferred← citeshttps://doi.org/10.1016/s0143-6228(97)00048-9openalex-snapshot
A neural network approach to the analysis of city systems
inferred← citeshttps://doi.org/10.1016/s0263-7855(96)00060-4openalex-snapshot
Comparison of structurally different allosteric modulators of muscarinic receptors by self-organizing neural networks
inferred← citeshttps://doi.org/10.1016/s0304-3800(02)00258-2openalex-snapshot
Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters
inferred← citeshttps://doi.org/10.1016/s0893-6080(96)00051-2openalex-snapshot
Variational Approach to Unsupervised Learning Algorithms of Neural Networks
inferred← citeshttps://doi.org/10.1016/s0893-6080(99)00034-9openalex-snapshot
Conformal self-organization for continuity on a feature map
inferred← citeshttps://doi.org/10.1021/ac00109a039openalex-snapshot
Modeling of Property Prediction from Multicomponent Analytical Data Using Different Neural Networks
inferred← citeshttps://doi.org/10.1023/a:1008188322239openalex-snapshot
Steroid binding by antibodies and artificial receptors: Exploration of theoretical methods to determine the origins of binding affinities and specificities
inferred← citeshttps://doi.org/10.1023/a:1008194019144openalex-snapshot
Computational methods for the structural alignment of molecules
inferred← citeshttps://doi.org/10.1023/a:1027276425268openalex-snapshot
The use of self-organizing neural networks in drug design
inferred← citeshttps://doi.org/10.1101/2020.12.31.424948openalex-snapshot
eSPRESSO: a spatial self-organizing-map clustering method for single-cell transcriptomes of various tissue structures using graph-based networks
inferred← citeshttps://doi.org/10.1109/icnn.1994.374265openalex-snapshot
Similarity analysis of molecules with self-organizing surfaces-An extension of the self-organizing map
inferred← citeshttps://doi.org/10.1162/089976698300017485openalex-snapshot
Weight-Value Convergence of the SOM Algorithm for Discrete Input
inferred← citeshttps://doi.org/10.1177/0160017610387297openalex-snapshot
Effects of Irregular Topology in Spherical Self-Organizing Maps
inferred← citeshttps://doi.org/10.1186/s12859-023-05355-4openalex-snapshot
eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.1186/s13321-026-01223-4openalex-snapshot
Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes
inferred← citeshttps://doi.org/10.1214/aoap/1035463330openalex-snapshot
A self-organizing cluster process
inferred← citeshttps://doi.org/10.14311/nnw.2013.23.025openalex-snapshot
NEURAL NETWORK WORLD: OPTIMIZED SPIRAL SPHERICAL SOM (OSS-SOM)
inferred← citeshttps://doi.org/10.48780/publications.aston.ac.uk.00000672openalex-snapshot
Topographic mappings and feed-forward neural networks
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529041openalex-snapshot
Additional file 1 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529044openalex-snapshot
Additional file 3 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529047openalex-snapshot
Additional file 5 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529050openalex-snapshot
Additional file 7 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529053openalex-snapshot
Additional file 10 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529056openalex-snapshot
Additional file 11 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
inferred← citeshttps://doi.org/10.6084/m9.figshare.23529062openalex-snapshot
Additional file 12 of eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
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