Information Technology Reference
In-Depth Information
240. Werbos, P.: Generalization of backpropagation with application to a recurrent
gas market model. Neural Networks 1(4), 339-356 (1988)
241. Williams, R., Zipser, D.: A learning algorithm for continually running fully
recurrent neural networks. Neural Computation 1, 270-280 (1989)
242. Williams, R., Zipser, D.: Gradient-based learning algorithms for recurrent net-
works and their computational complexity. In: Chauvin, Y., Rumelhart, D.E.
(eds.) Back-propagation: Theory, Architectures and Applications. Erlbaum,
Hillsdale (1995)
243. Woodward, W.A.: Approximation of Type IV Probability Integral. Master's
thesis, Texas Tech University (1971)
244. Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering:
Theory and its application to image segmentation. IEEE Trans. on Pattern
Analysis and Machine Learning 15(11), 1101-1113 (1993)
245. Xu, D.: Entropy and Information Potential for Neural Computation. Ph.D.
thesis, University of Florida, USA (1999)
246. Xu, D., Principe, J.C.: Learning from Examples with Quadratic Mutual In-
formation. In: IEEE Signal Processing Society Workshop Neural Networks for
Signal Processing VIII, pp. 155-164 (1998)
247. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. on Neural
Networks 16(3), 645-678 (2005)
248. Xu, X.: Dbscan,
http://ifsc.ualr.edu/xwxu
249. Hochberg, Y., Tamhane, A.C.: Multiple Comparison Procedures. John Wiley
& Sons (1997)
250. Yuan, H., Xiong, F., Huai, X.: A method for estimating the number of hid-
den neurons in feed-forward neural networks based on information entropy.
Computers and Electronics in Agriculture 40(1-3), 57-64 (2003)
251. Yuan, L., Kesavan, H.: Minimum entropy and information measure. IEEE
Trans. Systems, Man, and Cybernetics 28(3), 488-491 (1998)
252. Zhang, G.: Neural networks for classification: a survey. IEEE Trans. on Sys-
tems, Man and Cybernetics, Part C (Applications and Reviews) 30(4), 451-
462 (2000)
253. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An ecient clustering
method for very large databases. In: ACM SIGMOD Workshop on Research
Issues on Data Mining and Knowledge Discovery, Montreal, Canada, pp. 103-
114 (1996)
254. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: A new data clustering al-
gorithm and its applications. Data Mining and Knowledge Discovery 1(2),
141-182 (1997)