Machine learning for reliability engineering and safety applications: Review of current status and future opportunities
Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …
industries. Its impact is profound, and several fields have been fundamentally altered by it …
Learning from accidents: Interactions between human factors, technology and organisations as a central element to validate risk studies
Many industries are subjected to major hazards, which are of great concern to stakeholders
groups. Accordingly, efforts to control these hazards and manage risks are increasingly …
groups. Accordingly, efforts to control these hazards and manage risks are increasingly …
[BOOK][B] Projection-based clustering through self-organization and swarm intelligence: combining cluster analysis with the visualization of high-dimensional data
MC Thrun - 2018 - books.google.com
This open access book covers aspects of unsupervised machine learning used for
knowledge discovery in data science and introduces a data-driven approach to cluster …
knowledge discovery in data science and introduces a data-driven approach to cluster …
Forty years of color quantization: a modern, algorithmic survey
ME Celebi - Artificial Intelligence Review, 2023 - Springer
Color quantization (cq), the reduction of the number of distinct colors in a given image with
minimal distortion, is a common image processing operation with various applications in …
minimal distortion, is a common image processing operation with various applications in …
Principles for constructing three-way approximations of fuzzy sets: A comparative evaluation based on unsupervised learning
Three-way approximations of fuzzy sets are an important scheme of granular computing, by
abstracting a fuzzy set to its discrete three option-alternatives which adhere to human …
abstracting a fuzzy set to its discrete three option-alternatives which adhere to human …
Self-organizing maps for clustering hyperspectral images on-board a cubesat
Hyperspectral remote sensing reveals detailed information about the optical response of a
scene. Self-Organizing Maps (SOMs) can partition a hyperspectral dataset into clusters, both …
scene. Self-Organizing Maps (SOMs) can partition a hyperspectral dataset into clusters, both …
Self-organizing maps, theory and applications
Abstract The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo
Kohonen in the early 80s. It acts as a non supervised clustering algorithm as well as a …
Kohonen in the early 80s. It acts as a non supervised clustering algorithm as well as a …
Examining global warming factors using self-organizing map and Granger causality network: a case from South Korea
Background Understanding and patterning the possible causal variables of global warming
is attributed to the development of effective prevention and mitigation strategies for climate …
is attributed to the development of effective prevention and mitigation strategies for climate …
Vector batch SOM algorithms for multi-view dissimilarity data
Multi-view data has become fairly important since large amounts of information are
constantly generated from different sources. So far, most multi-view research on …
constantly generated from different sources. So far, most multi-view research on …
[HTML][HTML] Uncovering high-dimensional structures of projections from dimensionality reduction methods
Projections are conventional methods of dimensionality reduction for information
visualization used to transform high-dimensional data into low dimensional space. If the …
visualization used to transform high-dimensional data into low dimensional space. If the …