Opening the black box -Data driven visualization of neural networks

Proceedings of the IEEE Visualization Conference

Volumn , Issue , 2005, Pages 49-

  Tzeng, Fan Yin ^a   Ma, Kwan Liu ^a  

^a University of California Davis   (United States)

Author keywords

Artificial Neural Network; Classification; Information Visualization; Machine Learning; Visualization Application

Indexed keywords

BLACK BOXES; INFORMATION VISUALIZATION; VISUALIZATION APPLICATIONS;

CLASSIFICATION (OF INFORMATION); COMPUTER HARDWARE; COMPUTER SOFTWARE; DATA PROCESSING; LEARNING SYSTEMS; NEUROLOGY; PROBLEM SOLVING; VISUALIZATION;

NEURAL NETWORKS;

EID: 33749429989     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/VIS.2005.73     Document Type: Conference Paper

References (31)

1. Kjersti Aas and Line Eikvil. Text categorization: A survey. Technical Report 941, Norwegian Computing Center, 1999.
2. Robert Andrews, Joachim Diederich, and Alan B. Tickle. A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems, 8(6):373-389, 1995.
3. Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144-152, 1992.
4. William W. Cohen and Yoram Singer. Context-sensitive learning methods for text categorization. ACM Transactions on Information Systems (TOIS), 17(2):141-173, 1999.
5. Mark Craven and Jude Shavlik. Visualizing learning and computation in artificial neural networks. International Journal on Artificial Intelligence Tools, 1(3):399-425, 1992.
6. Ioannis Dimopoulos, J. Chronopoulos, Aikaterini Chronopoulou-Sereli, and Sovan Lek. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in athens city (greece). Ecological Modelling, 120(2-3): 157-165, 1999.
7. Harris Drucker, Donghui Wu, and Vladimir N. Vapnik. Support vecfor machines for spam categorization. IEEE Transaction on Neural Networks, 10(5): 1048-1054, September 1999.
8. Wlodzislaw Duch. Visualization of hidden node activity in neural networks: I. visualization methods. In Proceedings of the International Conference on Artificial Intelligence and Soft Computing, pages 3843, 2004.
9. Wlodzislaw Duch. Visualization of hidden node activity in neural networks: Ii. application to rbf networks. In Proceedings of the International Conference on Artificial Intelligence and Soft Computing, pages 44-49, 2004.
10. Ying-Huey Fua, Matthew O. Ward, and Elke A. Rundensteiner. Hierarchical parallel coordinates for exploration of large datasets. In IEEE Visualization 1999 Proceedings, pages 43-50,1999.
11. Stephan I. Gallant. Connectionist expert systems. Communications of the ACM, 31(2): 152-169, 1988.
12. G. David Garson. Interpreting neural-network connection weights. AI. Expert, 6(4):41-51, 1991.
13. Erol Gelenbe, Yutao Feng, K. Ranga, and R. Krishnan. Neural networks for volumetric MR imaging of the brain, pages 194-202, August 1996.
14. Muriel Gevrey, Ioannis Dimopoulos, and Sovan Lek. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160:249-264, 2003.
15. A.T.C. Goh. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3):143-151, 1995.
16. Lawrence O. Hall, Amine M. Bensaid, Laurence P. Clarke, Robert P. Velthuizen, Martin S. Silbiger, and James C. Bezdek. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. In IEEE Transactions on Neural Networks, volume 3, pages 672-682, September 1992.
17. Helwig Hauser, Florian Ledermann, and Helmut Doleisch. Angular brushing of extended parallel coordinates. In In Proceedings of IEEE Symposium on Information Visualization 2002 (Info Vis 2002), pages 127-130, 2002.
18. Alfred Inselberg. The plane with parallel coordinates. The Visual Computer, 1(2):69-92, 1985.
19. Savio L. Y. Lam and Dik Lun Lee. Feature reduction for neural network based text categorization. In Proceedings of the Sixth International Conference on Database Systems for Advanced Applications (DASFAA), pages 195-202, 1999.
20. Holger R. Maier, Graeme C. Dandy, and Michael D. Burch. Use of artificial neural networks for modelling cyanobacteria anabaena spp. in the river murray, south australia. Ecological Modelling, 105:257-272, 1998.
21. Julian D. Olden and Donald A. Jackson. Illuminating the"black box" a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1-2):135-150, 2002.
22. Leonid I. Perlovsky. Neural Networks and Intellect: Using Model-Based Concepts. Oxford University Press, 2000.
23. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by backpropagation error. Nature, 323:533-536, 1986.
24. Michele Scardi and Lawrence W. Harding. Developing an empirical model of phytoplankton primary production: a neural network case study. Ecological Modelling, 120:220-233, 1999.
25. Jonas Sjberg, Qinghua Zhang, Lennart Ljung, Albert Benveniste, Bernard Delyon, Pierre-Yves Glorennec, Hkan Hjalmarsson, and Anatoli Juditsky. Nonlinear black-box modeling in system identification: a unified overview. Automatica (Journal of IFAC), 31(12): 1691-1724, 1995.
26. Matthew J. Streeter, Matthew O. Ward, and Sergio A. Alvarez. Nvis: An interactive visualization tool for neural networks. In Proceedings of SPIE Symposium on Visual Data Exploration and Analysis VII, pages 234-241, 2001.
27. A. H. Sung. Ranking importance of input parameters of neural networks. Expert Systems with Applications, 15:405-411, 1998.
28. Alan B. Tickle, Robert Andrews, Mostefa Golea, and Joachim Diederich. The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks, 9(6): 1057-1068, 1998.
29. Fan-Yin Tzeng, Eric B. Lum, and Kwan-Liu Ma. An intelligent system approach to higher-dimensional classification of volume data. IEEE Transactions on Visualization and Computer Graphics, 11(3):273-284, 2005.
30. Paul Werbos. Beyond Regression: New Tools for Prediction and Analysis In the Behavioral Sciences. PhD thesis, Department of Applied Mathematics, Harvard University, 1974.
31. Erik Wiener, Jan O. Pedersen, and Andreas S. Weigend. A neural network approach to topic spotting. In Proceedings of SDAIR '95, 4th Annual Symposium on Document Analysis and Information Retrieval, pages 317-332, 1995.