scholarly journals Adaptive Metric Learning Vector Quantization for Ordinal Classification

2012 ◽  
Vol 24 (11) ◽  
pp. 2825-2851 ◽  
Author(s):  
Shereen Fouad ◽  
Peter Tino

Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. In ordinal LVQ, unlike in nominal LVQ, the class order information is used during training in selecting the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype-based models in general are more amenable to interpretations and can often be constructed at a smaller computational cost than alternative nonlinear classification models. Experiments demonstrate that the proposed ordinal LVQ formulations compare favorably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.

2006 ◽  
Vol 6 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Muhammad Fahad Umer ◽  
M. Sikander Hayat Khiyal

Author(s):  
Piotr Boniecki ◽  
Małgorzata Idzior-Haufa ◽  
Agnieszka Pilarska ◽  
Krzysztof Pilarski ◽  
Alicja Kolasa-Wiecek

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.


1998 ◽  
Vol 6 (1) ◽  
pp. 65-74 ◽  
Author(s):  
L. Pesu ◽  
P. Helistö ◽  
E. Ademovič ◽  
J.-C. Pesquet ◽  
A. Saarinen ◽  
...  

Author(s):  
D T Pham ◽  
E J Bayro-Corrochano

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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