Learning in Feed-Forward Artificial Neural Networks I

Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Other kinds of supervised ANN have developed their own techniques. Such is the case of Radial Basis Function (RBF) networks (Poggio & Girosi, 1989). There has been also considerable work on the development of adhoc learning methods based on evolutionary algorithms.

[1]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[2]  Vijayan Sugumaran.  Intelligent support systems : knowledge management , 2002 .

[3]  Jiebo Luo,et al.  Bayesian Networks for Image Understanding , 2007 .

[4]  Kin Meng Sam,et al.  Ontology-Based Shopping Agent for E-Marketing , 2010, Int. J. Intell. Inf. Technol..

[5]  Vijayan Sugumaran.  Intelligent Information Technologies and Applications , 2008 .

[6]  Xin Yao,et al.  A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..

[7]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[8]  Tom Tollenaere,et al.  SuperSAB: Fast adaptive back propagation with good scaling properties , 1990, Neural Networks.

[9]  Vijayan Sugumaran.  Intelligent Information Technologies: Concepts, Methodologies, Tools and Applications , 2007 .

[10]  José Luis Rojo-Álvarez,et al.  A Complex Support Vector Machine Approach to OFDM Coherent Demodulation , 2007 .

[11]  Marie-Luce Bourguet,et al.  An Overview of Multimodal Interaction Techniques and Applications , 2009 .

[12]  Xuan F. Zha,et al.  Artificial Intelligence and Integrated Intelligent Information Systems: Emerging Technologies and Applications , 2006 .

[13]  Jeffrey L. Goldberg,et al.  Newshound Revisited: The Intelligent Agent that Retrieves News Postings , 2002 .

[14]  V. Sugumaran.  The Inaugural Issue of the International Journal of Intelligent Information Technologies , 2005 .

[15]  Ankush Mittal,et al.  Bayesian Network Technologies: Applications and Graphical Models , 2007 .

[16]  Michael Lawo,et al.  IT-ASSIST: Towards Usable Applications for Elderly People , 2011, Int. J. Ambient Comput. Intell..

[17]  Ricardo Imbert,et al.  Combining Requirements Engineering and Agents , 2005 .

[18]  Yongjian Fu,et al.  Improving Mobile Web Navigation Using N-Grams Prediction Models , 2007, Int. J. Intell. Inf. Technol..

[19]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.