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Título
Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
Autor(es)
Materia
Computer Science
Fecha de publicación
2007
Editor
Springer Science + Business Media
Citación
Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4693, pp. 435-443.
Resumen
This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way.
URI
ISBN
978-3-540-74826-7 (Print) / 978-3-540-74827-4 (Online)
ISSN
0302-9743 (Print) / 1611-3349 (Online)
Colecciones
- BISITE. Congresos [288]