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MC-SC2 Publications about: neural networks
Articles in journal or book chapters
  1. D. West, P. Mangiameli, R. Rampal, and V. West. Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application. European Journal of Operational Research, 162(2):532-551, 2005. Keyword(s): decision support systems, medical informatics, neural networks, bootstrap aggregate models, ensemble strategies. [bibtex-entry]

  2. P. Mangiameli, D. West, and R. Rampal. Model selection for medical diagnosis decision support systems. Decision Support Systems, 36(3):247-259, 2004. Keyword(s): model selection, medical diagnosis, neural networks, bootstrap aggregating models, diverse ensembles, baseline ensembles, bagging models. [bibtex-entry]

  3. S. Sohn and C. Dagli. Ensemble of Evolving Neural Networks in Classification. Neural Processing Letters, 19(3):191-203, 2004. Keyword(s): classification, combining classifiers, data mining, genetic algorithm, neural networks. [bibtex-entry]

  4. S. Bose. Multilayer statistical classifiers. Computational Statistics & Data Analysis, 42(4):685-701, 2003. Keyword(s): classification, cubic splines, discriminant analysis, neural networks, CART, simulation. [bibtex-entry]

  5. M. Hüsken and P. Stagge. Recurrent neural networks for time series classification. Neurocomputing, 50:223-235, 2003. Keyword(s): recurrent neural network, classification, time series, multitask learning, generalization. [bibtex-entry]

  6. S. Draghici. On the capabilities of neural networks using limited precision weights. Neural Networks, 15:395-414, 2002. Keyword(s): integer weights, VLSI implementation, VC-complexity. [bibtex-entry]

  7. J. Mandziuk and L. Shastri. Incremental class learning approach and its application to handwritten digit recognition. Information Sciences: An International Journal, 141:193-217, 2002. Keyword(s): incremental class learning, catastroghic interference problem, supervised learning, spatio-temporal representation, pattern recognition, handwritten digit recognition, neural networks. [bibtex-entry]

  8. P. Pendharkar. A Computational study on the performance of artificial neural networks under changing structural design and data distribution. European Journal of Operational Research, 138:155-177, 2002. Keyword(s): artificial neural networks, discriminant analysis. [bibtex-entry]

  9. A. Agarwal, J. Davis, and T. Ward. Supporting ordinal four-state classification decisions using neural networks. Information Technology and Management, 2(1):5-26, 2001. Keyword(s): decision support systems, neural networks, ordinal multi-state classification, financial distress. [bibtex-entry]

  10. S. Dreiseitl, L. Ohno-Machado, H. Kittler, S. Vinterbo, H. Billhardt, and M. Binder. A comparison of machine learning methods for the diagnosis of pigmented skin lesions. Journal of Biomedical Informatics, 34, 2001. Keyword(s): machine learning, decision support, image classification, neural networks, support vector machines. [bibtex-entry]

  11. C.-L. Chen, D. Kaber, and P. Dempsey. A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk. Applied Ergonomics, 31:269-282, 2000. Keyword(s): neural networks, ergonomics, low-back disorders, job classification. [bibtex-entry]

  12. E. Gad, A. Atiya, S. Shaheen, and A. El-Dessouki. A new algorithm for learning in piecewise-linear neural networks. Neural Networks, 13:485-505, 2000. Keyword(s): neural networks, convergence, function approximation. [bibtex-entry]

  13. H. Ishibuchi and M. Nii. Neural networks for soft decision making. Fuzzy Sets and Systems, 115:121-140, 2000. Keyword(s): neural networks, soft decision, reject option, interval arithmetic, fuzzy arithmetic. [bibtex-entry]

  14. M. Kattan and R. Cooper. A simulation of factors affecting machine learning techniques: An examination of partitioning and class proportions. Omega - The International Journal of Management Science, 28:501-512, 2000. Keyword(s): machine learning, neural networks, ID3, CART, recursive partitioning, simulation. [bibtex-entry]

  15. R. Sexton and R. Dorsey. Reliable classification using networks: A genetic algorithm and backpropagation comparison. Decision Support Systems, 30:11-22, 2000. Keyword(s): neural networks, genetic algorithm, backpropagation, decision support systems, classification, artificial intelligence. [bibtex-entry]

  16. H. Hruschka and M. Natter. Comparing performance of feedforward neural nets and k-means for cluster-based market segmentation. European Journal of Operational Research, 114(2):346-353, 1999. Keyword(s): neural networks, marketing, k-means, cluster analysis, market segmentation. [bibtex-entry]

  17. I. Jagielska, C. Matthews, and T. Whitfort. An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems. Neurocomputing, 24:37-54, 1999. Keyword(s): automated knowledge acquisition, fuzzy rule extraction, fuzzy classification systems, neural networks, genetic algorithm, rough sets. [bibtex-entry]

  18. P. Mangiameli and D. West. An improved neural classification network for the two-group problem. Computers & Operations Research, 26:443-460, 1999. Keyword(s): neural networks, mixture of experts, two-group classification. [bibtex-entry]

  19. W. Penny and S. Roberts. Bayesian neural networks for classification: How useful is the evidence framework?. Neural Networks, 12:877-892, 1999. Keyword(s): classification, multi-layer perception, bayesian, regularization, feature selection, model selection, committees. [bibtex-entry]

  20. M. Zadeh and P. Nassery. Application of quadratic neural networks to seismic signal classification. Physics on the Earth and Planetary Interiors, 113:103-110, 1999. Keyword(s): neural networks, classification, Prony method, ARMA model. [bibtex-entry]

  21. N. Maglaveras, T. Stamkopoulos, K. Diamantaras, C. Papas, and M. Strintzis. ECG pattern recognition and classification using non-linear transformations and neural networks: A review. International Journal of Medical Informatics, 52:191-208, 1998. Keyword(s): biomedical signal processing, ECG processing, non-linear principal component analysis, neural networks, ischemia detection. [bibtex-entry]

  22. V. Gupta, J.-G. Chen, and M. Murtaza. A learning vector quantization neural network model for the classification of industrial construction projects. Omega - The International Journal of Management Science, 25(6):715-727, 1997. Keyword(s): neural networks, construction industry, application, classification, decision making, learning vectr optimization. [bibtex-entry]

  23. A. Kehagias and V. Petridis. Predictive modular neural networks for time series classification. Neural Networks, 10(1):31-49, 1997. Keyword(s): modular neural networks, hierarchical neural networks, adaptive neural networks, times series, classification, prediction, Bayesian decision rules. [bibtex-entry]

  24. B. Stiles and J. Ghosh. Habituation based neural networks for spatio-temporal classification. Neurocomputing, 15:273-307, 1997. Keyword(s): dynamic neural networks, habituation, classification, spatio-temporal signals, recurrent networks. [bibtex-entry]

  25. J. Denton and M. Hung. A comparison of nonlinear optimization methods for supervised learning in multilayer feedforward neural networks. European Journal of Operational Research, 93:358-368, 1996. Keyword(s): neural networks training, nonlinear programming. [bibtex-entry]

  26. D. Alpsan, M. Towsey, O. Ozdamar, C. Tsoi, and D. Ghista. Efficacy of modified backpropagation and optimization methods on a real-world medical problem. Neural Networks, 8(6):945-962, 1995. Keyword(s): neural networks, multilayer perceptron, backpropagation, optimisation, auditory evoked potencial, pattern classification. [bibtex-entry]



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Last modified: Fri Dec 15 07:05:48 2006
Author: Juscelino Dias.

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