Title :
Intelligent Sensory Evaluation Based on Support Vector Machines
Author :
Ting, LIU ; Wei, DONG ; Dingrong, MOU ; Ronggang, GONG ; Xiaoli, Bai
Author_Institution :
New Star Comput. Eng. Center, Ocean Univ. of China, Qingdao
Abstract :
Support vector machines (SVMs) are provided with great abilities of analyzing data with the characteristics of small sample-sets, high dimension, nonlinear, high noise. They are applicable to deal with machine learning problems of industries. The paper brought forward to taking advantage of multi-classification SVMs to evaluate the sensory qualities of products according to the data feature of such industries. The simulative experiments were done on the factual data-sample offered by a tobacco factory. The results validated the practical performance of SVMs learning models, which could satisfy the necessary of product designs.
Keywords :
learning (artificial intelligence); pattern classification; product design; support vector machines; tobacco industry; intelligent sensory evaluation; machine learning; multiclassification SVM; product design; support vector machine; tobacco factory; Competitive intelligence; Fuzzy logic; Intelligent sensors; Learning systems; Machine intelligence; Machine learning; Machinery production industries; Statistical analysis; Statistical learning; Support vector machines;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.869
