Neural network classification

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R. Feraud and F. Clerot, A methodology to explain neural network classification, Neural Networks, 15(2), 2002, 237-246. 

Moreover, several classification methods can be found among the artificial neural networks. Classification model performance is evaluated by classification parameters, both for fitting and predictive purposes. 

Kvasnicka, V, Sklenak, . and Pospichal, J. (1993b) Neural network classification of inductive and resonance effects of substituents. /. Am. Chem. Soc., 115, 1495-1500. 

Hepner, G.F., Logan, T., Ritter, N., and Bryant, N., 1990. Artificial Neural Network classification using a minimal training set comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing, 56,469-473... 

G. W. Stockton. Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry (Elsevier) 2003, 62, 971-985. 

Dreiseitl, S., Ohno-Machado, L., 2003. Logistic regression and artificial neural network classification models a methodology review. J. Biomed. Inform. 35, 352—359. 

Jerebko AK, Malley JD, Franaszek M et al. (2003a) Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets. Acad Radiol 10 154-160... 

OreSki, S., Zupan, J. and GlaviC, P., 2001, Neural network classification of phase equilibrium methods, Chem. Biochem. Eng. Q. 15, 3. 

Zhao, R., Xu, G., Yue, B., Lieblich, H.M., Zhang, Y., Artificial neural network classification based on capillary electrophoresis of urinary nucleosides for the clinical diagnosis of tumors. J. Chromatogr. A, 828, 489-496 (1998). 

Murray, S.O., Mercado, E. and Roitblat, H.L. (1998b) The neural network classification of false killer whale Pseudorca crassidens) vocahzations. Journal of the Acoustical Society of America, 104 3626—3633. 

ANN has made its presence felt in all related aspeets in textile from the fiber cultivation to garment manufacturing. Pattern recognition is one of the areas where ANN has played a significant role. Attempts have been made to identify fibers as well as trash using neural networks and NIR spectrophotometry. Even the animal fibers, merino and mohair have been recognized and classified by She et al. [2]. Cotton colors are also classified based on chromatic differences by the use of neural network. Classification of trash, such as leaf, bark and seed coat, the content of each of these trash particles is important in the sense to deeide the cleaning process. 

Steps 5-7 are necessarily iterative and it should be expected that several modifications to both training data picking and/or selection of input data (attribute cubes) are required before a satisfactory result is achieved. In order to speed up the process, the workflow can be first run on a representative subvolume. After the training data have been picked, checked and parameters optimised, the neural network classification can then be run on the full volume of interest (Figure 3). 

S.K. Addy (1998) Neural network classification method heljjs seismic interval interpretation. Oif Gas Journal 96(37), 47-59. 

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