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SETIONO,RUDY
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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.