



@book {2395,
	title = {Error entropy minimization for LSTM training},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4131 LNCS - I},
	year = {2006},
	note = {Conference code: 68317Cited By (since 1996): 3Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; Department of Informatics, IT-Networks and Multimedia Group, University of Beira Interior, Covilh{\~a}, Portugal; email: lfbaa@di.ubi.ptReferences: Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780;Gers, F., Schmidhuber, J., Cummins, F., Learning to forget: Continual prediction with LSTM (2000) Neural Computation, 12 (10), pp. 2451-2471; 
Gers, F., Schmidhuber, J., Recurrent nets that time and count (2000) Proc. IJCNN{\textquoteright}2000, Int. Joint Conf. on Neural Networks, , Como, Italy; 
P{\'e}rez-Ortiz, J., Gers, F., Eck, D., Schmidhuber, J., Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets (2003) Neural Networks, 16 (2), pp. 241-250; 
Haykin, S., (1999) Neural Networks: A Comprehensive Foundation, 2nd Edition, , Prentice Hall; 
Erdogmus, D., Principe, J., An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems (2002) IEEE Trans. Signal Processing, 50 (7), pp. 1780-1786; 
Santos, J., Alexandre, L., Sereno, F., Marques De S{\'a}, J., Optimization of the error entropy minimization algorithm for neural network classification (2004) ANNIE 2004, Intelligent Engineering Systems Through Artificial Neural Networks, 14, pp. 81-86. , St.Louis, USA, ASME Press Series; 
Santos, J., Alexandre, L., Marques De S{\'a}, J., The error entropy minimization algorithm for neural network classification (2004) Proceedings of the 5th International Conference on Recent Advances in Soft Computing, pp. 92-97. , Lofti, A., ed.: Nottingham, United Kingdom; 
Silva, L., Marques De S{\'a}, J., Alexandre, L., Neural network classification using Shannon{\textquoteright}s entropy (2005) 13th European Symposium on Artificial Neural Networks - ESANN 2005, pp. 217-222. , Bruges, Belgium
},
	month = {2006///},
	pages = {244 - 253},
	address = {Athens},
	abstract = {In this paper we present a new training algorithm for the Long Short-Term Memory (LSTM) recurrent neural network. This algorithm uses entropy instead of the usual mean squared error as the cost function for the weight update. More precisely we use the Error Entropy Minimization approach, were the entropy of the error is minimized after each symbol is present to the network. Our experiments show that this approach enables the convergence of the LSTM more frequently than with the traditional learning algorithm. This in turn relaxes the burden of parameter tuning since learning is achieved for a wider range of parameter values. The use of EEM also reduces, in some cases, the number of epochs needed for convergence. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Algorithms, Convergence of numerical methods, Cost function, entropy, Error Entropy Minimization, Errors, Long Short-Term Memory (LSTM), Neural networks, Optimization},
	isbn = {03029743 (ISSN); 3540386254 (ISBN); 9783540386254 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749837658\&partnerID=40\&md5=5f40159a3f1faf4760405dd581e79bf7},
	author = {Alexandre, L.A. and Marques De S{\'a}, J.P.}
}
