Classification of Textual Documents Using Learning Vector Quantization

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classifying the degree of compost maturation based on easily accessible graphic information encoded in the digital images. The research resulted in the development of original software for quick and easy assessment of compost maturity. The generated SOFM neural model was the kernel of the constructed IT system.

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Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization

Technology and Health Care ◽

10.3233/thc-1998-6108 ◽

1998 ◽

Vol 6 (1) ◽

pp. 65-74 ◽

Cited By ~ 32

Author(s):

L. Pesu ◽

P. Helistö ◽

E. Ademovič ◽

J.-C. Pesquet ◽

A. Saarinen ◽

...

Keyword(s):

Vector Quantization ◽

Wavelet Packet ◽

Learning Vector Quantization ◽

Respiratory Sounds ◽

Wavelet Packet Decomposition

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Neural Classifiers for Automated Visual Inspection

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1243/pime_proc_1994_208_166_02 ◽

1994 ◽

Vol 208 (2) ◽

pp. 83-89 ◽

Cited By ~ 14

Author(s):

D T Pham ◽

E J Bayro-Corrochano

Keyword(s):

Vector Quantization ◽

Visual Inspection ◽

Vision System ◽

Back Propagation ◽

Learning Vector Quantization ◽

Multi Layer Perceptron ◽

Automated Visual Inspection ◽

Valve Stem

This paper discusses the application of a back-propagation multi-layer perceptron and a learning vector quantization network to the classification of defects in valve stem seals for car engines. Both networks were trained with vectors containing descriptive attributes of known flaws. These attribute vectors (‘signatures’) were extracted from images of the seals captured by an industrial vision system. The paper describes the hardware and techniques used and the results obtained.

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The classification of fetus gender on ultrasound images using learning vector quantization (LVQ)

2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI) ◽

10.1109/miceei.2014.7067329 ◽

2014 ◽

Cited By ~ 1

Author(s):

I Md. Dendi Maysanjaya ◽

Hanung Adi Nugroho ◽

Noor Akhmad Setiawan

Keyword(s):

Vector Quantization ◽

Learning Vector Quantization ◽

Ultrasound Images

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Implementation Learning Vector Quantization(LVQ) for Chronic Kidney Disease Classification

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v09.i02.p11 ◽

2020 ◽

Vol 9 (2) ◽

pp. 241

Author(s):

I Gst Bgs Bayu Adi Pramana ◽

I Made Widiartha ◽

Luh Gede Astuti

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Vector Quantization ◽

Waste Product ◽

Learning Vector Quantization ◽

The Body ◽

Disease Classification ◽

Optimal Learning ◽

Fully Functioning

Chronic kidney disease is a disruption in the function of the kidney organs. When the kidneys are no longer fully functioning, the body is filled with water and a waste product called uremia. As a result, the body or legs will experience swelling and feel tired quickly because the body needs clean blood. Therefore, impaired kidney function should not be underestimated because it can be fatal. Researchers have conducted research related to the classification of kidney disease to find out what symptoms can cause kidney disease. One method that can be used for classification is the Learning Vector Quantization (LVQ) method. In this study, the LVQ algorithm was applied to classify chronic kidney disease. From the research results, the highest accuracy is 81.667% with the optimal learning rate is 0.002.

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