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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

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Cover of 'Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Look and Feel What and How Recurrent Self-Organizing Maps Learn
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    Chapter 2 Self-Organizing Mappings on the Flag Manifold
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    Chapter 3 Self-Organizing Maps with Convolutional Layers
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    Chapter 4 Cellular Self-Organising Maps - CSOM
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    Chapter 5 A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering
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    Chapter 6 SOM-Based Anomaly Detection and Localization for Space Subsystems
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    Chapter 7 Self-Organizing Maps in Earth Observation Data Cubes Analysis
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    Chapter 8 Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps
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    Chapter 9 Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms
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    Chapter 10 Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features
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    Chapter 11 Robust Adaptive SOMs Challenges in a Varied Datasets Analytics
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    Chapter 12 Detection of Abnormal Flights Using Fickle Instances in SOM Maps
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    Chapter 13 LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison
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    Chapter 14 When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns
  16. Altmetric Badge
    Chapter 15 Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors
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    Chapter 16 A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data
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    Chapter 17 Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm
  19. Altmetric Badge
    Chapter 18 Investigation of Activation Functions for Generalized Learning Vector Quantization
  20. Altmetric Badge
    Chapter 19 Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks
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    Chapter 20 Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization
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    Chapter 21 Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework
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    Chapter 22 Soft Subspace Topological Clustering over Evolving Data Stream
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    Chapter 23 Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention
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    Chapter 24 Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers
  26. Altmetric Badge
    Chapter 25 Subspace Quantization on the Grassmannian
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    Chapter 26 Variants of Fuzzy Neural Gas
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    Chapter 27 Autoencoders Covering Space as a Life-Long Classifier
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    Chapter 28 Progressive Clustering and Characterization of Increasingly Higher Dimensional Datasets with Living Self-organizing Maps
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    Chapter 29 A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI
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    Chapter 30 Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer
  32. Altmetric Badge
    Chapter 31 Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets
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    Chapter 32 Searching for the Origins of Life – Detecting RNA Life Signatures Using Learning Vector Quantization
  34. Altmetric Badge
    Chapter 33 Simultaneous Display of Front and Back Sides of Spherical SOM for Health Data Analysis
Attention for Chapter 19: Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
14 X users

Citations

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8 Dimensions

Readers on

mendeley
22 Mendeley
Summary X Dimensions citations
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Chapter title
Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks
Chapter number 19
Book title
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
Published in
arXiv, June 2019
DOI 10.1007/978-3-030-19642-4_19
Book ISBNs
978-3-03-019641-7, 978-3-03-019642-4
Authors

Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann, Saralajew, Sascha, Holdijk, Lars, Rees, Maike, Villmann, Thomas

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Researcher 3 14%
Other 2 9%
Student > Master 2 9%
Professor 1 5%
Other 1 5%
Unknown 7 32%
Readers by discipline Count As %
Computer Science 12 55%
Engineering 2 9%
Unknown 8 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2019.
All research outputs
#6,143,214
of 23,128,387 outputs
Outputs from arXiv
#128,028
of 951,412 outputs
Outputs of similar age
#109,874
of 350,933 outputs
Outputs of similar age from arXiv
#4,451
of 29,028 outputs
Altmetric has tracked 23,128,387 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 951,412 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 350,933 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 29,028 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.

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