Electronic nose and chiral-capillary electrophoresis in evaluation of the quality changes in commercial green tea leaves during a long-term storage, Talanta, vol.129, issue.2014, pp.32-38 ,
DOI : 10.1016/j.talanta.2014.04.044
Metal oxide SAW E-nose employing PCA and ANN for the identification of binary mixture of DMMP and methanol, Sensors and Actuators B: Chemical, vol.200, pp.147-156, 2014. ,
DOI : 10.1016/j.snb.2014.04.065
Statistical and Syntactic Pattern Recognition, pp.151-196 ,
DOI : 10.1016/B978-0-12-409545-8.00006-6
Evaluation of oxygen exposure levels and polyphenolic content of red wines using an electronic panel formed by an electronic nose and an electronic tongue, Food Chemistry, vol.155, issue.15, pp.155-91 ,
DOI : 10.1016/j.foodchem.2014.01.021
A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule, Pattern Recognition Letters, vol.25, issue.1, pp.63-71, 2004. ,
DOI : 10.1016/j.patrec.2003.09.002
Parameter determination of support vector machine and feature selection using simulated annealing approach, Applied Soft Computing, vol.8, issue.4, pp.1505-1512, 2008. ,
DOI : 10.1016/j.asoc.2007.10.012
Hydroxymethylfurfuraldehyde and amylase contents in Australian honey, Food Chemistry, vol.119, issue.3, pp.1000-1005, 2010. ,
DOI : 10.1016/j.foodchem.2009.07.057
Optimizing an Electronic Nose for Analysis Honey from Different Nectar Sources, Sensor Letters, vol.11, issue.6, pp.1145-1148, 2013. ,
DOI : 10.1166/sl.2013.2872
A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable and Sustainable Energy Reviews, pp.102-109, 2014. ,
Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification, Pattern Recognition, vol.39, issue.7, pp.1215-1229, 2006. ,
DOI : 10.1016/j.patcog.2006.02.001
URL : http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0031320306000410&originContentFamily=serial&_origin=article&_ts=1480274325&md5=647d4c17a8db00b232c670409ae65d5d
Optimizing the Hyper-parameters for SVM by Combining Evolution Strategies with a Grid Search Ruiming, pp.712-721, 2006. ,
Genetic algorithm based defect identification system, Expert Systems with Applications, vol.18, issue.1, pp.17-25, 2000. ,
DOI : 10.1016/S0957-4174(99)00046-9
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Computers in Biology and Medicine, vol.43, issue.5, pp.576-586 ,
DOI : 10.1016/j.compbiomed.2013.01.020
VC-dimension and structural risk minimization for the analysis of nonlinear ecological models, Applied Mathematics and Computation, vol.176, issue.1, pp.166-176, 2006. ,
DOI : 10.1016/j.amc.2005.09.050
The Nature of Statistical Learning Theory, Data Mining and Knowledge Discovery, vol.6, pp.1-47, 1999. ,
Vapnik-Chervonenkis dimension of recurrent neural networks, Discrete Applied Mathematics, vol.86, issue.1, pp.63-79, 1998. ,
DOI : 10.1016/S0166-218X(98)00014-6
Local minima in hierarchical structures of complex-valued neural networks, Neural Networks, vol.43, pp.1-7, 2013. ,
DOI : 10.1016/j.neunet.2013.02.002