The influence of parameter initialization on the training time and accuracy of a nonlinear regression model
In order to construct a nonlinear regression model we have to accurately (in some sense) initialize parameters of the model. In this work we performed comparison of several widely used methods and several newly developed approached for initialization of parameters of a regression model, represented...
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| Veröffentlicht in: | Journal of communications technology & electronics Jg. 61; H. 6; S. 646 - 660 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Moscow
Pleiades Publishing
01.06.2016
Springer Springer Nature B.V |
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| ISSN: | 1064-2269, 1555-6557 |
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| Abstract | In order to construct a nonlinear regression model we have to accurately (in some sense) initialize parameters of the model. In this work we performed comparison of several widely used methods and several newly developed approached for initialization of parameters of a regression model, represented as a decomposition in a linear dictionary of some parametric functions (sigmoids). We proposed a general deterministic approach for initialization, providing repeatability of results, reduction of a learning time and in some cases increase of a regression model accuracy; we developed two new algorithms (based on a piecewise-linear approximation and based on local properties of approximable dependency) in the framework of the proposed approach; we developed randomized initialization algorithm (spherical initialization) for effective approximation of high-dimensional dependencies; we improved the classical initialization method SCAWI (by locating centers of sigmoids in sample points), providing a regression model accuracy improvement on specific classes of dependencies (smooth functions and discontinuous functions with a number of local peculiarities in an input domain) when using RProp algorithm for learning; we performed comparison of classical and newly proposed initialization methods and highlighted the most efficient ones. |
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| AbstractList | In order to construct a nonlinear regression model we have to accurately (in some sense) initialize parameters of the model. In this work we performed comparison of several widely used methods and several newly developed approached for initialization of parameters of a regression model, represented as a decomposition in a linear dictionary of some parametric functions (sigmoids). We proposed a general deterministic approach for initialization, providing repeatability of results, reduction of a learning time and in some cases increase of a regression model accuracy; we developed two new algorithms (based on a piecewise-linear approximation and based on local properties of approximable dependency) in the framework of the proposed approach; we developed randomized initialization algorithm (spherical initialization) for effective approximation of high-dimensional dependencies; we improved the classical initialization method SCAWI (by locating centers of sigmoids in sample points), providing a regression model accuracy improvement on specific classes of dependencies (smooth functions and discontinuous functions with a number of local peculiarities in an input domain) when using RProp algorithm for learning; we performed comparison of classical and newly proposed initialization methods and highlighted the most efficient ones. In order to construct a nonlinear regression model we have to accurately (in some sense) initialize parameters of the model. In this work we performed comparison of several widely used methods and several newly developed approached for initialization of parameters of a regression model, represented as a decomposition in a linear dictionary of some parametric functions (sigmoids). We proposed a general deterministic approach for initialization, providing repeatability of results, reduction of a learning time and in some cases increase of a regression model accuracy; we developed two new algorithms (based on a piecewise-linear approximation and based on local properties of approximable dependency) in the framework of the proposed approach; we developed randomized initialization algorithm (spherical initialization) for effective approximation of high-dimensional dependencies; we improved the classical initialization method SCAWI (by locating centers of sigmoids in sample points), providing a regression model accuracy improvement on specific classes of dependencies (smooth functions and discontinuous functions with a number of local peculiarities in an input domain) when using RProp algorithm for learning; we performed comparison of classical and newly proposed initialization methods and highlighted the most efficient ones. Keywords: nonlinear regression, approximation, neural networks, parameter initialization, SCAWI algorithm, RProp algorithm, error back propagation algorithm DOI: 10.1134/S106422691606005X |
| Audience | Academic |
| Author | Burnaev, E. V. Erofeev, P. D. |
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| CitedBy_id | crossref_primary_10_1007_s10462_021_10033_z crossref_primary_10_1134_S1064226919120039 crossref_primary_10_1007_s10472_017_9545_y crossref_primary_10_1016_j_petrol_2020_107504 crossref_primary_10_1016_j_cageo_2019_02_002 crossref_primary_10_1016_j_petrol_2019_05_055 crossref_primary_10_1016_j_advengsoft_2016_09_001 crossref_primary_10_3390_sym13050758 |
| Cites_doi | 10.1016/j.neucom.2003.10.014 10.1002/wics.53 10.1134/S0005117913100044 10.1007/3-7643-7356-3_19 10.1109/IJCNN.1990.137819 10.1137/060657704 10.1016/0893-6080(95)00071-2 10.1007/s005210050006 10.1109/72.728357 10.1093/oso/9780198523963.001.0001 |
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| Keywords | parameter initialization nonlinear regression approximation neural networks RProp algorithm SCAWI algorithm error back propagation algorithm |
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| SubjectTerms | Accuracy Algorithms Approximation Artificial neural networks Communications Engineering Communications equipment Communications technology Comparative analysis Decomposition Dependence Dictionaries Engineering Experiments Learning Mathematical analysis Mathematical models Mathematical Models and Computational Methods Mean square errors Networks Neural networks Nonlinear systems Nonlinearity Regression Regression analysis Studies Support vector machines |
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| Title | The influence of parameter initialization on the training time and accuracy of a nonlinear regression model |
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