Neural Networks For Data Mining

Neural networks have become standard and important tools for data mining. This chapter provides an overview of neural network models and their applications to data mining tasks.We provide historical development of the field of neural networks and present three important classes of neural models including feedforward multilayer networks, Hopfield networks, and Kohonen's self-organizing maps. Modeling issues and applications of these models for data mining are discussed.
Key words: neural networks, regression, classification, prediction, clustering
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Subscribe and save
Springer+ Basic
€32.70 /Month
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (France)
eBook EUR 42.79 Price includes VAT (France)
Softcover Book EUR 52.74 Price includes VAT (France)
Hardcover Book EUR 52.74 Price includes VAT (France)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Similar content being viewed by others

Neural Networks – State of Art, Brief History, Basic Models and Architecture
Chapter © 2016

Neural Networks and Deep Learning
Chapter © 2022

Perceptron and Neural Networks
Chapter © 2023
References
- Adya M., Collopy F. (1998), How effective are neural networks at forecasting and prediction? a review and evaluation. Journal of forecasting ; 17:481-495. ArticleGoogle Scholar
- Agrawal D., Schorling C. (1996), Market share forecasting: an empirical comparison of artificial neural networks and multinomial logit model. Journal of Retailing ; 72:383-407. ArticleGoogle Scholar
- Ahn H., Choi E., Han I. (2007), Extracting underlying meaningful features and canceling noise using independent component analysis for direct marketing Expert Systems with Applications, ; 33: 181-191 ArticleGoogle Scholar
- Azoff E. M. (1994), Neural Network Time Series Forecasting of Financial Markets. Chichester: John Wiley & Sons, . Google Scholar
- Bishop M. (1995), Neural Networks for Pattern Recognition. Oxford: Oxford University Press, . Google Scholar
- Boone D., Roehm M. (2002), Retail segmentation using artificial neural networks. International Journal of Research in Marketing ; 19:287-301. ArticleGoogle Scholar
- Brockett P.L., Xia X.H., Derrig R.A. (1998), Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. The Journal of Risk and Insurance ; 65: 24 ArticleGoogle Scholar
- Changchien S.W., Lu T.C. (2001), Mining association rules procedure to support on-line recommendation by customers and products fragmentation. Expert Systems with Applications ; 20: Google Scholar
- Chen T., Chen H. (1995), Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems, Neural Networks ; 6:911-917. ArticleGoogle Scholar
- Chen F.L., Liu S.F. (2000), A neural-network approach to recognize defect spatial pattern in semiconductor fabrication. IEEE Transactions on Semiconductor Manufacturing ; 13:366-37 ArticleGoogle Scholar
- Chen S.K., Mangiameli P., West D. (1995), The comparative ability of selforganizing neural networks to define cluster structure. Omega ; 23:271-279. ArticleGoogle Scholar
- Chen H., Zhang Y., Houston A.L. (1998), Semantic indexing and searching using a Hopfield net. Journal of Information Science ; 24:3-18. ArticleGoogle Scholar
- Cheng B., Titterington D. (1994), Neural networks: a review from a statistical perspective. Statistical Sciences ; 9:2-54. ArticleMATHMathSciNetGoogle Scholar
- Chen K.Y., Wang, C.H. (2007), Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management ; 28:215-226. ArticleGoogle Scholar
- Chiang W.K., Zhang D., Zhou L. (2006), Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression. Decision Support Systems ; 41:514-531. ArticleGoogle Scholar
- Church K. B., Curram S. P. (1996), Forecasting consumers’ expenditure: A comparison between econometric and neural network models. International Journal of Forecasting ; 12:255-267 ArticleGoogle Scholar
- Ciampi A., Lechevallier Y. (1997), Statistical models as building blocks of neural networks. Communications in Statistics: Theory and Methods ; 26:991-1009. ArticleMATHMathSciNetGoogle Scholar
- Crone S.F., Lessmann S., Stahlbock R. (2006), The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research ; 173:781-800 ArticleMATHMathSciNetGoogle Scholar
- Cybenko G. (1989), Approximation by superpositions of a sigmoidal function. Mathematical Control Signals Systems ; 2:303-314. ArticleMATHMathSciNetGoogle Scholar
- Dai Y., Nakano Y. (1998), Recognition of facial images with low resolution using a Hopfield memory model. Pattern Recognition ; 31:159-167. ArticleGoogle Scholar
- Dasu T., Johnson T. (2003), Exploratory Data Mining and Data Cleaning. New Jersey: Wiley, . BookMATHGoogle Scholar
- De Groot D., Wurtz D. (1991), Analysis of univariate time series with connectionist nets: A case study of two classical examples. Neurocomputing ;3:177-192. ArticleGoogle Scholar
- Deboeck G., Kohonen T. (1998), Visual Explorations in Finance with Selforganizing Maps. London: Springer-Verlag, . Google Scholar
- Delen D., Sharda R., Bessonov M. (2006), Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks Accident Analysis and Prevention ; 38:434-444. ArticleGoogle Scholar
- Dhar V., Chou D. (2001), A comparison of nonlinear methods for predicting earnings surprises and returns. IEEE Transactions on Neural Networks ; 12:907-921. ArticleGoogle Scholar
- Dia H. (2001), An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operation Research ; 131:253-261. ArticleMATHGoogle Scholar
- Dittenbach M., Rauber A., Merkl, D. (2002), Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocompuing ; 48:199-216. ArticleMATHGoogle Scholar
- Doganis P., Alexandridis A., Patrinos P., Sarimveis H. (2006), Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering ; 75:196-204. ArticleGoogle Scholar
- Dutot A.L., Rynkiewicz J., Steiner F.E., Rude J. (2007), A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions Modelling and Software; 22:1261-1269. Google Scholar
- Dutta S., Shenkar S. (1993), “Bond rating: a non-conservative application of neural networks.” In Neural Networks in Finance and Investing, Trippi, R., and Turban, E., eds. Chicago: Probus Publishing Company. Google Scholar
- Enke D., Thawornwong S. (2005), The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications ; 29:927-940. ArticleGoogle Scholar
- Evans O.V.D. (1997), Discovering associations in retail transactions using neural networks. ICL Systems Journal ; 12:73-88. Google Scholar
- Fahlman S., Lebiere C. (1990), “The cascade-correlation learning architecture.” In Advances in Neural Information Processing Systems, Touretzky, D., ed. . Google Scholar
- Fletcher R. (1987), Practical Methods of Optimization 2 nd . Chichester: John Wiley & Sons, . Google Scholar
- Frean M. (1990), The Upstart algorithm: a method for constructing and training feed-forward networks. Neural Computations ; 2:198-209. ArticleGoogle Scholar
- Funahashi K. (1998), Multilayer neural networks and Bayes decision theory. Neural Networks ; 11:209-213. ArticleGoogle Scholar
- Gallinari P., Thiria S., Badran R., Fogelman-Soulie, F. (1991), On the relationships between discriminant analysis and multilayer perceptrons. Neural Networks ; 4:349-360. ArticleGoogle Scholar
- Geman S., Bienenstock E., Doursat T. (1992), Neural networks and the bias/variance dilemma. Neural Computation ; 5:1-58. ArticleGoogle Scholar
- Gorr L. (1994), Research prospective on neural network forecasting. International Journal of Forecasting ; 10:1-4. ArticleGoogle Scholar
- He H., Wang J., Graco W., Hawkins S. (1997), Application of neural networks to detection of medical fraud. Expert Systems with Applications ; 13:329-336. ArticleGoogle Scholar
- Hebb D.O. (1949), The Organization of Behavior. New York: Wiley. Google Scholar
- Hinton G.E. (1992), How neural networks learn from experience. Scientific American ;9:145-151. Google Scholar
- Hornik K., Stinchcombe M., White H. (1989), Multilayer feedforward networks are universal approximators. Neural Networks ; 2:359-366. ArticleGoogle Scholar
- Hopfield J.J. (2558), (1982), Neural networks and physical systems with emergent collective computational abilities. Proceedings of National Academy of Sciences; 79:2554-. Google Scholar
- Hopfield J.J., Tank D.W. (1985), Neural computation of decisions in optimization problems. Biological Cybernetics ; 52:141-152. MATHMathSciNetGoogle Scholar
- Hu J.Q., Rose, E. (1995), On-line fuzzy modeling by data clustering using a neural network. Advances in Process Control. , 4, 187-194. Google Scholar
- Huang J.S., Liu H.C. (2004), Object recognition using genetic algorithms with a Huang Z. Chen, H., Hsu, C.J. Chen, W.H. and Wu, S., Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems ; 37:543-558 Google Scholar
- Hopfield’s neural model (1997). Expert Systems with Applications 1997; 13:191-199. Google Scholar
- Jain L.C., Vemuri V.R. (1999), Industrial Applications of Neural Networks. Boca Raton: CRC Press, . Google Scholar
- Kiang M.Y., Hu, M.Y., Fisher D.M. (2006), An extended self-organizing map network for market segmentation—a telecommunication example Decision Support Systems ; 42:36-47. Google Scholar
- Kiang M.Y., Kulkarni U.R., Tam K.Y. (1995), Self-organizing map network as an interactive clustering tool-An application to group technology. Decision Support Systems ; 15:351-374. ArticleGoogle Scholar
- Kim T., Kumara S.R.T., (1997), Boundary defect recognition using neural networks. International Journal of Production Research; 35:2397-2412. ArticleMATHGoogle Scholar
- Kim T.Y., Oh K.J., Sohn K., Hwang C. (2004), Usefulness of artificial neural networks for early warning system of economic crisis. Expert Systems with Applications ; 26:583-590. ArticleGoogle Scholar
- Kirkos E., Spathis C., Manolopoulos Y., (2007), Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications ; 32: 995-1003. ArticleGoogle Scholar
- Kiviluoto K. (1998), Predicting bankruptcy with the self-organizing map. Neurocomputing ; 21:203-224. ArticleGoogle Scholar
- Klein B.D., Rossin D. F. (1999), Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy. Omega ; 27:569-582. ArticleGoogle Scholar
- Kohonen T. (1982), Self-organized formation of topologically correct feature maps. Biological Cybernetics ; 43:59-69. ArticleMATHMathSciNetGoogle Scholar
- Kolehmainen M., Martikainen H., Ruuskanen J. (2001), Neural networks and periodic components used in air quality forecasting. Atmospheric Environment ; 35:815-825. ArticleGoogle Scholar
- Law R. (2000), Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management ; 21:331-340. ArticleGoogle Scholar
- Lee D.L. (2002), Pattern sequence recognition using a time-varying Hopfield network. IEEE Transactions on Neural Networks ; 13:330-343. ArticleGoogle Scholar
- Lewis O.M., Ware J.A., Jenkins D. (1997), A novel neural network technique for the valuation of residential property. Neural Computing and Applications ; 5:224-229. ArticleGoogle Scholar
- Li W.J., Lee T., (2002), Object recognition and articulated object learning by accumulative Hopfield matching. Pattern Recognition; 35:1933-1948. ArticleMATHGoogle Scholar
- Lim G.S., Alder M., Hadingham P. (1992), Adaptive quadratic neural nets. Pattern Recognition Letters ; 13: 325-329. ArticleGoogle Scholar
- Lisboa P.J.G., Edisbury B., Vellido A. (2000), Business Applications of Neural Networks : The State-of-the-art of Real-world Applications. River Edge: World Scientific, . Google Scholar
- McCulloch W., Pitts W. (1943), A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics ; 5:115-133. ArticleMATHMathSciNetGoogle Scholar
- Min S.H., Lee J., Han I. (2006), Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications ; 31: 652-660. ArticleGoogle Scholar
- Minsky M. L., Papert S. A. (1969), Perceptrons. MA: MIT press, . MATHGoogle Scholar
- Miyake S., Kanaya F. (1991), A neural network approach to a Bayesian statistical decision problem. IEEE Transactions on Neural Networks ; 2:538-540. ArticleGoogle Scholar
- Mozer M.C., Wolniewics R. (2000), Predicting subscriber dissatisfaction and improving retention in the wireless telecommunication. IEEE Transactions on Neural Networks ; 11:690-696 ArticleGoogle Scholar
- Nag A.K., Mitra A. (2002), Forecasting daily foreign exchange rates using genetically optimized neural networks. Journal of Forecasting ; 21:501-512. ArticleGoogle Scholar
- Nelson M., Hill T., Remus T., O’Connor, M. (1999), Time series forecasting using neural networks: Should the data be deseasonalized first? Journal of Forecasting ; 18:359-367. ArticleGoogle Scholar
- O’Connor N., Madden M.G. (2006), A neural network approach to predicting stock exchange movements using external factors. Knowledge-Based Systems ; 19:371-378. ArticleGoogle Scholar
- Paik J.K., Katsaggelos, A.K. (1992), Image restoration using a modified Hopfield neural network. IEEE Transactions on Image Processing ; 1:49-63. ArticleGoogle Scholar
- Pajares G., Cruz J.M., Aranda, J. (1998), Relaxation by Hopfield network in stereo image matching. Pattern Recognition ; 31:561-574. ArticleGoogle Scholar
- Panda C., Narasimhan V. (2007), Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling ; 29:227-236. ArticleGoogle Scholar
- Parker D.B. (1985), Learning-logic: Casting the cortex of the human brain in silicon, Technical Report TR-47, Center for Computational Research in Economics and Management Science, MIT. Google Scholar
- Palmer A., Montaño J.J., Sesé, A. (2006), Designing an artificial neural network for forecasting tourism time series. Tourism Management ; 27: 781-790. ArticleGoogle Scholar
- Partovi F.Y., Anandarajan M. (2002), Classifying inventory using an artificial neural network approach. Computers and Industrial Engineering ; 41:389-404. ArticleGoogle Scholar
- Petersohn H. (1998), Assessment of cluster analysis and self-organizing maps. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. ; 6:139-149. ArticleMATHGoogle Scholar
- Prybutok V.R., Yi J., Mitchell D. (2000), Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations. European Journal of Operational Research ; 122:31-40. ArticleMATHGoogle Scholar
- Qi M. (2001), Predicting US recessions with leading indicators via neural network models. International Journal of Forecasting ; 17:383-401. ArticleGoogle Scholar
- Qi M., Zhang G.P. (2001), An investigation of model selection criteria for neural network time series forecasting. European Journal of Operational Research ; 132:666-680. ArticleMATHGoogle Scholar
- Qiao F., Yang H., Lam, W.H.K. (2001), Intelligent simulation and prediction of traffic flow dispersion. Transportation Research, Part B ; 35:843-863. ArticleGoogle Scholar
- Raudys S. (1998), Evolution and generalization of a single neuron: I., Single-layer perceptron as seven statistical classifiers Neural Networks ; 11:283-296. Google Scholar
- Raudys S. (1998), Evolution and generalization of a single neuron: II., Complexity of statistical classifiers and sample size considerations. Neural Networks ; 11:297-313. ArticleGoogle Scholar
- Raviwongse R., Allada V., Sandidge T. (2000), Plastic manufacturing process selection methodology using self-organizing map (SOM)/fuzzy analysis. International Journal of Advanced Manufacturing Technology; 16:155-161. ArticleGoogle Scholar
- Reed R. (1993), Pruning algorithms-a survey. IEEE Transactions on Neural Networks ; 4:740-747. ArticleGoogle Scholar
- Remus W., O’Connor M. (2001), “Neural networks for time series forecasting.” In Principles of Forecasting: A Handbook for Researchers and Practitioners, Armstrong, J. S. ed. Norwell:Kluwer Academic Publishers, 245-256. Google Scholar
- Reutterer T., Natter M. (2000), Segmentation based competitive analysis with MULTICLUS and topology representing networks. Computers and Operations Research; 27:1227-1247. ArticleMATHGoogle Scholar
- Richard, M. (1991), D., Lippmann, R., Neural network classifiers estimate Bayesian aposteriori probabilities. Neural Computation ; 3:461-483. ArticleGoogle Scholar
- Ripley A. (1993), “Statistical aspects of neural networks.” In Networks and Chaos - Statistical and Probabilistic Aspects, Barndorff-Nielsen, O. E., Jensen J. L. and Kendall, W. S. eds. London: Chapman and Hall, 40-123. Google Scholar
- Ripley A. (1994), Neural networks and related methods for classification. Journal of Royal Statistical Society, Series B ; 56:409-456. MATHMathSciNetGoogle Scholar
- Roh T. H. (2007), Forecasting the volatility of stock price index. Expert Systems with Applications ; 33:916-922. ArticleGoogle Scholar
- Rosenblatt F. (1958), The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review ; 65:386-408. ArticleMathSciNetGoogle Scholar
- Rout S., Srivastava, S.P., Majumdar, J. (1998), Multi-modal image segmentation using a modified Hopfield neural network. Pattern Recognition ; 31:743-750. ArticleGoogle Scholar
- Rumelhart D.E., Hinton G.E., Williams R.J. (1986), “Learning internal representation by back-propagating errors.” In Parallel Distributed Processing: Explorations in the Microstructure of Cognition Press, Rumelhart, D.E., McCleland, J.L. and the PDP Research Group, eds. MA: MIT. Google Scholar
- Saad E.W., Prokhorov D.V., Wunsch, D.C. II. (1998), Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks; 9:456-1470. ArticleGoogle Scholar
- Salcedo-Sanz S., Santiago-Mozos R., Bousono-Calzon C. (2004), A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems. IEEE Transactions on System, Man and Cybernetics, Part B:108-116. Google Scholar
- Sarle W.S. (1994), Neural networks and statistical models. Poceedings of the Nineteenth Annual SAS Users Group International Conference, Cary, NC: SAS Institute,. Google Scholar
- Schumacher M., Robner R., Vach W. (1996), Neural networks and logistic regression: Part I., Computational Statistics and Data Analysis ; 21:661-682. ArticleMATHGoogle Scholar
- Smith K.A., Ng, A. (2003), Web page clustering using a self-organizing map of user navigation patterns. Decision Support Systems ; 35:245-256. ArticleGoogle Scholar
- Smith K.A., Willis R.J., Brooks M. (2000), An analysis of customer retention and insurance claim patterns using data mining: a case study. Journal of the Operational Research Society; 51:532-541. MATHGoogle Scholar
- Soulie F.F., Gallinari P. (1998), Industrial Applications of Neural Networks. River Edge, NJ: World Scientific. Google Scholar
- Suganthan P.N., Teoh E.K., Mital D.P. (1995), Self-organizing Hopfield network for attributed relational graph matching. Image and Vision Computing; 13:61-71. ArticleGoogle Scholar
- Sun Z.Z., Yu S. (1995), Improvement on performance of modified Hopfield neural network for image restoration. IEEE Transactions on Image processing; 4:683-692. ArticleGoogle Scholar
- Suykens J.A.K., Vandewalle J.P.L., De Moor B.L.R. (1996), Artificial Neural Networks for Modeling and Control of Nonlinear Systems. Boston: Kluwer. Google Scholar
- Swanson N.R., White H. (1995), A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business and Economic Statistics; 13;265-275. ArticleGoogle Scholar
- Tatem A.J., Lewis H.G., Atkinson P.M., Nixon M.S. (2002), Supre-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment; 79:1-14. ArticleGoogle Scholar
- Temponi C., Kuo Y.F., Corley H.W. (1999), A fuzzy neural architecture for customer satisfaction assessment. Journal of Intelligent & Fuzzy Systems; 7:173-183. Google Scholar
- Thieme R.J., Song M., Calantone R.J. (2000), Artificial neural network decision support systems for new product developement project selection. Journal of Marketing Research ; 37:543-558. ArticleGoogle Scholar
- Vach W., Robner R., Schumacher M. (1996), Neural networks and logistic regression: Part I. Computational Statistics and Data Analysis; 21:683-701. ArticleMATHGoogle Scholar
- Wang T., Zhuang X., Xing X. (1992), Robust segmentation of noisy images using a neural network model. Image Vision Computing; 10:233-240. ArticleGoogle Scholar
- Webb A.R., Lowe D., (1990), The optimized internal representation of multilayer classifier networks performs nonlinear discriminant analysis. Neural Networks; 3:367-375. ArticleGoogle Scholar
- Werbos P.J., (1974), Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, 1974. Google Scholar
- West D., (2000), Neural network credit scoring models. Computers and Operations Research; 27:1131-1152. ArticleMATHGoogle Scholar
- West P.M., Brockett P.L., Golden L.L., (1997), A comparative analysis of neural networks and statistical methods for predicting consumer choice. Marketing Science; 16:370-391. ArticleGoogle Scholar
- Widrow B., Hoff M.E., (1960), Adaptive switching circuits, 1960 IRE WESCON Convention Record, New York: IRE Part 4 1960:96-104. Google Scholar
- Widrow B., Rumelhart D.E., Lehr M.A., (1994), Neural networks: applications in industry, business and science, Communications of the ACM; 37:93-105. ArticleGoogle Scholar
- Wong B.K., Bodnovich T.A., Selvi Y., (1997), Neural network applications in business: A review and analysis of the literature (1988-1995). Decision Support Systems; 19:301-320. ArticleGoogle Scholar
- Young S.S., Scott P.D., Nasrabadi, N.M., (1997), Object recognition using multilayer Hopfield neural network. IEEE Transactions on Image Processing; 6:357-372. ArticleGoogle Scholar
- Zhang G.P., (2007), Avoiding Pitfalls in Neural Network Research. IEEE Transactions on Systems, Man, and Cybernetics; 37:3-16. ArticleGoogle Scholar
- Zhang G.P., Hu M.Y., Patuwo B.E., Indro D.C., (1999), Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research; 116:16-32. ArticleMATHGoogle Scholar
- Zhang G.P., Keil M., Rai A., Mann J., (2003), Predicting information technology project escalation: a neural network approach. European Journal of Operational Research 2003; 146:115-129. ArticleMATHGoogle Scholar
- Zhang G.P., Qi M. (2002), “Predicting consumer retail sales using neural networks.” In Neural Networks in Business: Techniques and Applications, Smith, K. and Gupta, J.eds. Hershey: Idea Group Publishing, 26-40. Google Scholar
- Zhang G.P., Patuwo E.P., Hu M.Y., (1998), Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting; 14:35-62. ArticleGoogle Scholar
- Zhang W., Cao Q., Schniederjans M.J., (2004), Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods. Decision Sciences; 35: 205-237. ArticleGoogle Scholar
- Zhu Z., He H., Starzyk J.A., Tseng, C., (2007), Self-organizing learning array and its application to economic and financial problems. Information Sciences; 177:1180-1192. ArticleGoogle Scholar
Author information
Authors and Affiliations
- Department of Managerial Sciences, Georgia State University, GA, USA G. Peter Zhang
- G. Peter Zhang