Self-Organizing Maps

By Gary G. Yen. IEEE International Conference on Intelligent Systems, 2011

2,803 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b21beb28a91db155f4c3ba1038bb95d368bfadd7

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In Artificial Intelligence

Citations in the corpus

Algorithms for hierarchical clustering: an overview, II (2017, background): “In its basic variant (Kohonen, 1984, 2001) is can be formulated in terms of k-means clustering subject to a set of interrelationships between the cluster centers (Murtagh and Fern´andez-Pajares, 1995).”
Unsupervised Learning of Basic Object Affordances from Object Properties (2009, methodology): “In Stage 1, we chose to use a self-organizing map (SOM) [11] to vector quantize the result space.”
Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data (2009, methodology): “A way to assess the quality of a kernel function in a supervised setting is by using the kernel alignment [16].”
Support vector machines in remote sensing: A review (2011, neutral): “Of particular interest would be comparison/fusion with algorithms such as self-organizing maps (Kohonen, 1997) that address efficiently high dimensionality problems and have already found fruitful ground in remote sensing (e.g., Hong et al., 2006; Goncalvesetal.,2008).”
On the Origin of Deep Learning (2017, methodology): “SOM is a powerful technique that is primarily used in reducing the dimension of data, usually to one or two dimensions (Germano, 1999).”
Flexible Self-Organizing Maps in kohonen 3.0 (2018, methodology): “For training SOMs several diﬀerent algorithms are used: The standard ones are the online and the batch algorithms (Kohonen 1995).”
A renaissance of neural networks in drug discovery (2016, methodology): “Kohonen ’ s Self-Organizing Maps (SOM) is a biology-inspired topology-preserving nonlinear dimensionality reduction method that can map molecules from multidimensional descriptor space onto a 2D grid of neurons [5].”
Low‐frequency nonlinearity and regime behavior in the Northern Hemisphere extratropical atmosphere (2017, background): “SOM, also known as Kohonen map [e.g., Kohonen, 2001] is an unsupervised neural network method.”
Machine Learning for Fluid Mechanics (2019, methodology): “An important version of this process is the self-organizing map (SOM) introduced by Kohonen (1995).”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, methodology): “Then SOM [118], [119] is used to perform DDoS attack detection according to the collected traffic flow features.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “For node localization of the WSNs, neural networks with self-organizing maps (i.e., Kohonen’s maps) and Learning Vector Quantization (LVQ) [141] have been used.”
Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces (2006, methodology): “Keywords: Dissimilarity representation; Prototype selection; Adaptive condensing; EM algorithm; Normal density based classifier; Nearest neighbour rule”
Perplexity-free t-SNE and twice Student tt-SNE (2018, background): “Several paradigms have been studied over time, like the reproduction of distances [2] or neighbourhoods [3, 4] from the data space to the embedding space.”
Multilayer Perceptron: Architecture Optimization and Training (2016, background): “The most of researchers treat the construction of neural architecture (structure) without finding the optimal neural architecture [7].”
Critical periods and catastrophic interference effects in the development of self-organizing feature maps. (2008, methodology): “Dynamic parameter changes over learning in the SOFM (Kohonen, 1982, 1995)”
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, methodology): “Various generative models have been developed and applied in different contexts, such as Auto-Encoder [ 48 ], Generative Adversarial Network (GAN) [ 49 ], Restricted Boltzmann Machine (RBM) [ 50 ], Deep Belief Network (DBN) [ 51 ], and Self-Organizing Map (SOM) [ 52 ].”