2019
DOI: 10.1016/j.cviu.2018.11.003
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Dynamic topology and relevance learning SOM-based algorithm for image clustering tasks

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Cited by 24 publications
(18 citation statements)
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“…e development of unsupervised learning is still immature, and the SOM algorithm still has some limitations [19][20][21][22] as follows:…”
Section: Limitations Of the Som Algorithmmentioning
confidence: 99%
“…e development of unsupervised learning is still immature, and the SOM algorithm still has some limitations [19][20][21][22] as follows:…”
Section: Limitations Of the Som Algorithmmentioning
confidence: 99%
“…en, according to the error between the output results and the actual expected results, we push back the optimization parameters and then continue to learn and adjust to achieve the goal of optimal learning. SOM is a new double-layer network and each input node is connected by weight w, so as to realize nonlinear dimension reduction mapping of input signals [28][29][30]. Topological invariance is maintained in mapping, that is, similar inputs in topological sense are mapped to the nearest output node.…”
Section: Som Model Learning Algorithmmentioning
confidence: 99%
“…Sentiment analysis methods based on machine learning are usually not limited by dictionaries and are mainly used [20][21][22][23][24][25]. e results showed that the effect of Naive Bayes is better.…”
Section: Introductionmentioning
confidence: 99%