Information Technology Reference
In-Depth Information
81. Gers, F., Schmidhuber, J.: Recurrent nets that time and count. In: Int. Joint
Conf. on Neural Networks, Como, Italy, pp. 189-194 (2000)
82. Gers, F., Schmidhuber, J., Cummins, F.: Learning to forget: Continual pre-
diction with LSTM. Neural Computation 12(10), 2451-2471 (2000)
83. Gish, H.: A probabilistic approach to the understanding and training of neural
network classifiers. In: Int. Conf. on Acoustics, Speech, and Signal Processing,
vol. 3, pp. 1361-1364 (1990)
84. Gokcay, E., Principe, J.: Information theoretic clustering. IEEE Trans. on
Pattern Analysis and Machine Intelligence 24(2), 158-171 (2002)
85. Guha, S., Rastogi, R., Shim, K.: CURE: an ecient clustering algorithm for
large databases. In: Int. Conf. on Management of Data, pp. 73-84 (1998)
86. Guha, S., Rastogi, R., Shim, K.: ROCK: A robust clustering algorithm for
categorical attributes. Information Systems 25(5), 345-366 (2000)
87. Gut, A.: Probability: A Graduate Course. Springer (2005)
88. Hall, E., Wise, G.: On optimal estimation with respect to a large family of
cost functions. IEEE Trans. Information Theory 37(3), 691-693 (1991)
89. Hampshire II, J., Pearlmutter, B.: Equivalence proofs for multilayer percep-
tron classifiers and the Bayesian discriminant function. In: Touretzky, D., El-
man, J., Sejnowsky, T., Hinton, G. (eds.) Proc. 1990 Connectionist Models
Summer School. Morgan Kaufmann, San Mateo (1990)
90. Hampshire II, J., Waibel, A.: A novel objective function for improved phoneme
recognition using time-delay neural networks. IEEE Trans. on Neural Net-
works 1(2), 216-228 (1990)
91. Harrison, M.: Introduction to Formal Language Theory. Addison-Wesley Series
in Computer Science (1978)
92. Hartuv, E., Schmitt, A., Lange, J., Meier-Ewert, S., Lehrachs, H., Shamir, R.:
An algorithm for clustering cDNAs for gene expression analysis. In: Annual
Conf. on Research in Computational Molecular Biology, pp. 188-197 (1999)
93. Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity.
Information Processing Letters 76(4-6), 175-181 (2000)
94. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning.
Springer (2001)
95. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn.
Prentice-Hall, New Jersey (1999)
96. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall
(2009)
97. He, Z., Xu, X., Deng, S.: K-ANMI: A mutual information based clustering
algorithm for categorical data. Computing Research Repository abs/cs/051
(2005)
98. Hero, A., Ma, B., Michel, O., Gorman, J.: Applications of entropic spanning
graphs. IEEE Signal Processing Magazine 19(5), 85-95 (2002)
99. Hild II, K., Erdogmus, D., Principe, J.: Blind source separation using Rényi's
mutual information. IEEE Signal Processing Letters 8, 174-176 (2001)
100. Hirose, A.: Complex-Valued Neural Networks. Springer (2006)
101. Hirose, A.: Complex-valued neural networks: The merits and their origins. In:
Int. Joint Conf. on Neural Networks, pp. 1209-1216 (2009)
102. Hochberg, Y., Tamhane, A.: Multiple Comparison Procedures. John Wiley &
Sons, Inc. (1987)
103. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computa-
tion 9(8), 1735-1780 (1997)
104. Hsin, H., Li, C., Sun, M., Sclabassi, R.: An adaptive training algorithm for
back-propagation neural networks. IEEE Trans. on Systems, Man, and Cy-
bernetics 25, 512-514 (1995)
