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Issue Date	Title	Author(s)
2002	A comparative study of centroid-based, neighborhood-based and statistical approaches for effective document categorization	Tam, V.; Santoso, A.; Setiono, R.
2000	A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders	Hayashi, Y.; Setiono, R. ; Yoshida, K.
1999	A connectionist approach to generating oblique decision trees	Setiono, R. ; Liu, H.
2005	A hybrid SOM-SVM approach for the zebrafish gene expression analysis	Wu, W.; Liu, X.; Xu, M.; Peng, J.-R.; Setiono, R.
2004	A hybrid SOM-SVM method for analyzing zebra fish gene expression	Wu, W.; Xin, L.; Min, X.; Jinrong, P. ; Setiono, R.
Jun-1994	A neural network construction algorithm with application to image compression	Setiono, R. ; Lu, G.
2009	A note on knowledge discovery using neural networks and its application to credit card screening	Setiono, R. ; Baesens, B.; Mues, C.
1-Jan-1997	A penalty-function approach for pruning feedforward neural networks	Setiono, R.
2004	An approach to generate rules from neural networks for regression problems	Setiono, R. ; Thong, J.Y.L.
2001	An effective method for generating multiple linear regression rules from artificial neural networks	Setiono, R. ; Azcarraga, A.
Mar-1998	Analysis of Hidden Representations by Greedy Clustering	Setiono, R. ; Liu, H.
2004	Applying the conjugate gradient method for text document categorization	Tam, V.; Setiono, R. ; Santoso, A.
2005	Automatic knowledge extraction from survey data: Learning M-of-N constructs using a hybrid approach	Setiono, R. ; Pan, S.-L. ; Hsieh, M.-H.; Azcarraga, A.
1995	Chi2: feature selection and discretization of numeric attributes	Liu, Huan ; Setiono, Rudy
2002	Combining neural network predictions for medical diagnosis	Hayashi, Y.; Setiono, R.
2004	Computational intelligence methods for rule-based data understanding	Duch, W.; Setiono, R. ; Zurada, J.M.
Feb-1996	Dimensionality reduction via discretization	Liu, H. ; Setiono, R.
2012	Discrete variable generation for improved neural network classification	Setiono, R. ; Seret, A.
1996	Effective data mining using neural networks	Lu, H. ; Setiono, R. ; Liu, H.
2005	Effective neural network pruning using cross-validation	Huynh, T.Q.; Setiono, R.

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