Spiking Self-organizing Maps for Classification Problem

Yusob, Bariah; Shamsuddin, Siti Mariyam; Hamed, Haza Nuzly Abdull

doi:10.1016/j.protcy.2013.12.162

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…The self-organizing map (SOM), invented by Kohonen [214], is an artificial neural network extensively employed for clustering and visualization in exploratory data analysis [215,216]. SOMs serve the purpose of reducing a complex, high-dimensional input space into a simpler, low-dimensional representation [217], finding applications across various fields [218].…”

Section: Self-organizing Mapmentioning

confidence: 99%

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Ibrahim,

Tapamo

2024

Informatics

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In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.

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Section: Self-organizing Mapmentioning

confidence: 99%

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Ibrahim,

Tapamo

2024

Informatics

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“…Interesting work on self-organization in SNNs has been accomplished in [30], in which the authors analyze SOM formation in a network of integrate-and-fire (IF) neurons on both synthetic data and a cancer dataset. In [31], a SNN is first trained to form a SOM of a cancer dataset, after which it is used to classify the data.…”

Section: Self-organizing Properties With Spiking Neural Networkmentioning

confidence: 99%

Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

Hazan

Saunders

Sanghavi

et al. 2019

Ann Math Artif Intell

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Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a self-organized grid of filters via cooperative and competitive excitatory-inhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network training, and (ii) switching the inhibition level from low to high (two-level ) after an initial training segment. During the labeling phase, the spiking activity generated by data with known labels is used to assign neurons to categories of data, which are then used to evaluate the network's classification ability on a held-out set of test data. Several biologically plausible evaluation rules are proposed and compared, including a population-level confidence rating, and an n-gram inspired method. The effectiveness of the proposed selforganized learning mechanism is tested using the MNIST benchmark dataset, as well as using images produced by playing the Atari Breakout game.

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“…The neuron of output layer is improves for spike neuron applied in the classification of cancer dataset and improves the processing speed of SOM in document [4] . Genetic algorithm is adopted to choose network weights in document [5] .…”

Section: Introductionmentioning

confidence: 99%

A Novel Blocked Winner Sequence Feature Extraction Method Based SOM For Large Set Chinese Character Recognition

Wang¹,

Zhang²,

Wang³

2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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A novel feature extraction method named blocked winner sequence feature extraction (BWS) method based on SOM is proposed for recognizing large set of Chinese characters. Firstly, a Chinese character sample is blocked to make sub block set according to certain principles; Secondly, every subblock is passed into a SOM network orderly for learning, and the location number of the winner for each sub-block is stored; Finally, location numbers of all winner neurons are combined into a sequence as the feature of the character. Compared with traditional SOM neural networks, the method using orderly combination of multiple winner neurons instead of single winner to represent feature can reduce the network size and computation as well as improve network capacity effectively. Using block can improve the anti-interference ability and the recognition accuracy of the network. The method is used for the extract feature of 3500 Chinese characters in GB2312 Chinese character library with Euclidean distance recognition, and the recognition accuracy reaches 99.897% and 93.274% respectively in the cases of adding 10% and 20% random noise.

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Spiking Self-organizing Maps for Classification Problem

Cited by 8 publications

References 4 publications

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

A Novel Blocked Winner Sequence Feature Extraction Method Based SOM For Large Set Chinese Character Recognition

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