Reformulated Parametric Learning Based on Ordinary Differential Equations.

Abstract : This paper presents a new parametric learning scheme, namely, Reformulated Parametric Learning (RPL). Instead of learning the parameters directly on the original model, this scheme reformulates the model into a simpler yet equivalent one, and all parameters are estimated on the reformulated model. While a set of simpler equivalent models can be obtained from deriving Equivalent Decomposition Models (EDM) through their associated ordinary differential equations, to achieve the simplest EDM is a combination optimization problem. For a preliminary study, we apply the RPL to a simple class of models, named 'Additive Pseudo-Exponential Models' (APEM). While conventional approaches have to adopt nonlinear programming to learn APEM, the proposed RPL can obtain equivalent solutions through Linear Least -Square (LLS) method. Numeric work confirms the better performance of the proposed scheme in comparing with conventional learning scheme.
Type de document :
Communication dans un congrès
Huang De-Shuang and Li Kang and Irwin George W. Computational Intelligence, International Conference on Intelligent Computing, ICIC 2006, Aug 2006, Kunming / China, Springer, 4114, pp.256-267, 2006, Lecture Notes in Computer Science. 〈10.1007/11816171_33〉
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https://hal.inria.fr/inria-00122738
Contributeur : Chine Publications Liama <>
Soumis le : jeudi 4 janvier 2007 - 15:59:00
Dernière modification le : mardi 24 avril 2018 - 13:30:01

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Shuang-Hong Yang, Bao-Gang Hu. Reformulated Parametric Learning Based on Ordinary Differential Equations.. Huang De-Shuang and Li Kang and Irwin George W. Computational Intelligence, International Conference on Intelligent Computing, ICIC 2006, Aug 2006, Kunming / China, Springer, 4114, pp.256-267, 2006, Lecture Notes in Computer Science. 〈10.1007/11816171_33〉. 〈inria-00122738〉

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