Wyniki wyszukiwania - BazTech

1

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Kaden M, Lange M., Nebel D, Riedel M, Geweniger T, Villmann T.

Foundations of Computing and Decision Sciences

|

2014

|

Vol. 39, No. 2

79--105

EN

Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.

2

Nursing logistics activities in massive services

Simić D.

Journal of Medical Informatics & Technologies

|

2011

|

Vol. 18

77--84

EN

Hybrid patient classification system in nursing logistics activities is discussed in this paper. Hybrid classification model is based on two of the most used competitive artificial neural network algorithms that use learning vector quantization models (LVQ) and self-organizing maps (SOM). In general, the history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. It is possible to discus three types of experimental results. First result type could be assessment for risk of falling measured by Mors scale and pressure sores risk measured by Braden scale. Both of them are assessed by LVQ. Hybrid LVQ-SOM model is used for second result type, which presents the time for nursing logistics activities. The third type is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.

3

Improving interpretability: combined use of LVQ and ARTMAP in decision support

Kwok H. F., Giorgi A., Raffone A.

Journal of Telecommunications and Information Technology

|

2005

|

nr 4

129-132

EN

The learning vector quantization (LVQ) network was used to classify the ECG ST segment into different morphological categories. Due to the lack of data in the ST elevation categories, the classifier was only trained to identify different types of ST depressions (horizontal, upsloping and downsloping). The accuracies were 91%, 85% and 65% respectively for the training, validation and testing data respectively. Despite the low accuracy for the testing data, most of the mis-classifications were downsloping ST depression being classified as horizontal ST depression. We concluded that more data and more training are needed in order to train the LVQ to recognize other morphological types of ST deviation and to improve the accuracy.

4

One-Dimensional Kohonen's Lvq Nets for Multidimensional Patterns Recognition

Skubalska-Rafajłowicz E.

International Journal of Applied Mathematics and Computer Science

|

2000

|

Vol. 10, no 4

767-778

EN

A new neural network based pattern recognition algorithm is proposed. The method consists in preprocessing the multidimensional data, using a space-filling curve based transformation into the unit interval, and employing Kohonen's vector quantization algorithms (of SOM and LVQ types) in one dimension. The space-filling based transformation preserves the theoretical Bayes risk. Experiments show that such an approach can produce good or even better error rates than the classical LVQ performed in a multidimensional space.