Multiple self-organizing maps for a visuo-motor system that uses multiple cameras with different fields of view

Okada, Nobuhiro; Qiu, Jinjun; Nakamura, Kenta; Kondo, Eiji

doi:10.1007/s10015-009-0639-4

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Multiple SOMs are an extension of the traditional single SOM. This learning process has been widely applied in several areas such as food analysis and remote sensing . Basically, an algorithm of the multiple SOMs is similar to that of the single SOM.…”

Section: Methodsmentioning

confidence: 99%

“…This learning process has been widely applied in several areas such as food analysis 13 and remote sensing. 22,23 Basically, an algorithm of the multiple SOMs is similar to that of the single SOM. The difference is that, in multiple SOMs, the training samples are separated into groups or classes to provide training on several different maps.…”

Section: Multiple Somsmentioning

confidence: 99%

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Application of multiple self‐organizing maps for classification of soil samples in Thailand according to their geographic origins

Krongchai

Funsueb

Jakmunee

et al. 2016

Journal of Chemometrics

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Multiple self‐organizing maps (SOMs) were applied to classify soil samples according to their geographic origins. The soil physical and chemical parameters, including textures, pH, and chemical nutrients, were analyzed and used for establishing the chemometric models. To determine the optimum size and arrangement of the maps, we adapted a growing self‐organizing map algorithm. To evaluate the reliability of the models, we calculated statistic indices based on the majority vote including percentage predictive ability, percentage model stability, and percentage correctly classified using a bootstrap methodology. For means of comparison, we also used linear discriminant analysis, quadratic discriminant analysis, partial least squares‐discriminant analysis, soft independent modeling of class analogy, counter propagation network, supervised Kohonen network, and k‐nearest neighbors. In comparison to a single SOM, multiple SOMs clearly provided better classification results. The extension of multiple SOMs also led to the best discrimination of the soil origins.

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Section: Methodsmentioning

confidence: 99%

Section: Multiple Somsmentioning

confidence: 99%

Application of multiple self‐organizing maps for classification of soil samples in Thailand according to their geographic origins

Krongchai

Funsueb

Jakmunee

et al. 2016

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…Simultaneoulsy, the U-matrix of the original algorithm shows higher distances between neighbors, which means that the mapping is not smooth. 2) Computer Vision: It takes advantage of self-organizing models for various purposes [6], [42], [49], [52], [55]. Here, we are interested in their capability to model a manifold of images [60].…”

Section: B Real Datamentioning

confidence: 99%

Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance

López‐Rubio

2013

IEEE Trans. Neural Netw. Learning Syst.

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The quality of self-organizing maps is always a key issue to practitioners. Smooth maps convey information about input data sets in a clear manner. Here a method is presented to modify the learning algorithm of self-organizing maps to reduce the number of topology errors, hence the obtained map has better quality at the expense of increased quantization error. It is based on avoiding maps that self-intersect or nearly so, as these states are related to low quality. Our approach is tested with synthetic data and real data from visualization, pattern recognition and computer vision applications, with satisfactory results.

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Higher Order Cognition using Computers: Learning Abstract Concepts with Recursive Graph-based Self Organizing Maps

Lim¹,

Kurashov²,

Bentley³

2016

Proceedings of the Artificial Life Conference 2016

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Multiple self-organizing maps for a visuo-motor system that uses multiple cameras with different fields of view

Cited by 3 publications

References 9 publications

Application of multiple self‐organizing maps for classification of soil samples in Thailand according to their geographic origins

Application of multiple self‐organizing maps for classification of soil samples in Thailand according to their geographic origins

Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance

Higher Order Cognition using Computers: Learning Abstract Concepts with Recursive Graph-based Self Organizing Maps

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