An incremental learning method for neural networks based on sensitivity analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5988 LNAI, Issue , 2010, Pages 42-50

  Pérez Sánchez, Beatriz ^a   Fontenla Romero, Oscar ^a   Guijarro Berdiñas, Bertha ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BATCH LEARNING; BATCH MODES; COMPUTATIONAL TIME; COMPUTING RESOURCE; INCREMENTAL LEARNING; LARGE DATABASE; LEARNING METHODS; LINEAR SYSTEM OF EQUATIONS; ONLINE VERSIONS; REAL-TIME LEARNING; THEORETICAL BASIS; TWO LAYERS;

ARTIFICIAL INTELLIGENCE; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; LINEAR SYSTEMS; SENSITIVITY ANALYSIS;

LEARNING ALGORITHMS;

EID: 77955037778     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14264-2_5     Document Type: Conference Paper

References (11)

1. Rumelhart, D.E., Hinton, G.E., Willian, R.J.: Learning Representations of Back-Propagation Errors. Nature 323, 533-536 (1986)
2. Castillo, E., Guijarro-Berdiñas, B., Fontenla-Romero, O., Alonso-Betanzos, A.: A very fast learning method for Neural Networks Based on Sensitivity Analysis. Journal of Machine Learning Research 7, 1159-1182 (2006)
3. Castillo, E., Fontenla-Romero, O., Alonso-Betanzos, A., Guijarro-Berdiñas, B.: A Global Optimum Approach for One-Layer Neural Networks. Neural Computation 14(6), 1429-1449 (2002)
4. Erdogmus, D., Fontenla-Romero, O., Principe, J.C., Alonso-Betanzos, A., Castillo, E.: Linear-Least-Squares Initialization of Multilayer Perceptrons Through Backpropagation of the Desired Response. IEEE Transactions on Neural Network 16(2), 325-337 (2005)
5. Fontenla-Romero, O., Erdogmus, D., Principe, J.C., Alonso-Betanzos, A., Castillo, E.: Linear least-squares based methods for neural networks learning. In: Kaynak, O., Alpaydn, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol.2714, pp. 84-91. Springer, Heidelberg (2003)
6. Bottou, L.: Stochastic Learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol.3176, pp. 146-168. Springer, Heidelberg (2004)
7. Moller, M.: Supervised learning on large redundant training sets. Neural Networks for Signal Processing II, 79-89 (1992)
8. LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient backProp. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol.1524, p. 9. Springer, Heidelberg (1998)
9. Collobert, R., Bengio, Y., Bengio, S.: Scaling large learning problems with hard parallel mixtures. International Journal of Pattern Recognition and Artificial Intelligence 17(3), 349-365 (2003)
10. Newman, D., Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/∼mlearn/MLRepository.html
11. Mangasarian, O.L., Ramakrishnan, R.: Data Mining Intitute, Computer Sciences Department, University of Wisconsin (1999), http://www.cs.wisc.edu/dmi