Statistical Perspective of SOM and CSOM for Hyper-Spectral Image Classification

Mallapragada, Srivatsa; Hung, Chih‐Cheng

doi:10.1109/igarss39084.2020.9324200

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…In his approach, SOM outperformed than the K-Means clustering analysis. Felix et al [ 36 ] and Srivatsa et al, [ 37 ] used the hybrid version of self organising map algorithm with Susi framework and Cellular Self-Organizing Map for classifying the hyper spectral datasets. A self-organization-based clustering network in MANET employing zone-based group mobility was proposed by Farooq et al in [ 40 ] to increase scalability and decrease additional energy consumption in the network.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Naskath¹,

Sivakamasundari²,

Begum

2022

Wireless Pers Commun

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Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.

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Section: Self-organizing Map (Som)mentioning

confidence: 99%