VOLK, Matej ;NAGODE, Marko ;FAJDIGA, Matija . Finite Mixture Estimation Algorithm for Arbitrary Function Approximation. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 58, n.2, p. 115-124, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/finite-mixture-estimation-algorithm-for-arbitrary-function-approximation/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2011.085.
Volk, M., Nagode, M., & Fajdiga, M. (2012). Finite Mixture Estimation Algorithm for Arbitrary Function Approximation. Strojniški vestnik - Journal of Mechanical Engineering, 58(2), 115-124. doi:http://dx.doi.org/10.5545/sv-jme.2011.085
@article{sv-jmesv-jme.2011.085,
author = {Matej Volk and Marko Nagode and Matija Fajdiga},
title = {Finite Mixture Estimation Algorithm for Arbitrary Function Approximation},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {58},
number = {2},
year = {2012},
keywords = {REBMIX algorithm; function approximations; finite mixtures; RBF networks; parameter estimation},
abstract = {The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the REBMIX algorithm, developed for the finite mixture density estimation. Since the REBMIX estimates the unknown parameters with the unique semiparametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the REBMIX algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the REBMIX algorithm can be applied successfully for the continuous function approximation.},
issn = {0039-2480}, pages = {115-124}, doi = {10.5545/sv-jme.2011.085},
url = {https://www.sv-jme.eu/article/finite-mixture-estimation-algorithm-for-arbitrary-function-approximation/}
}
Volk, M.,Nagode, M.,Fajdiga, M. 2012 June 58. Finite Mixture Estimation Algorithm for Arbitrary Function Approximation. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 58:2
%A Volk, Matej %A Nagode, Marko %A Fajdiga, Matija %D 2012 %T Finite Mixture Estimation Algorithm for Arbitrary Function Approximation %B 2012 %9 REBMIX algorithm; function approximations; finite mixtures; RBF networks; parameter estimation %! Finite Mixture Estimation Algorithm for Arbitrary Function Approximation %K REBMIX algorithm; function approximations; finite mixtures; RBF networks; parameter estimation %X The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the REBMIX algorithm, developed for the finite mixture density estimation. Since the REBMIX estimates the unknown parameters with the unique semiparametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the REBMIX algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the REBMIX algorithm can be applied successfully for the continuous function approximation. %U https://www.sv-jme.eu/article/finite-mixture-estimation-algorithm-for-arbitrary-function-approximation/ %0 Journal Article %R 10.5545/sv-jme.2011.085 %& 115 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 58 %N 2 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Volk, Matej, Marko Nagode, & Matija Fajdiga. "Finite Mixture Estimation Algorithm for Arbitrary Function Approximation." Strojniški vestnik - Journal of Mechanical Engineering [Online], 58.2 (2012): 115-124. Web. 20 Dec. 2024
TY - JOUR AU - Volk, Matej AU - Nagode, Marko AU - Fajdiga, Matija PY - 2012 TI - Finite Mixture Estimation Algorithm for Arbitrary Function Approximation JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2011.085 KW - REBMIX algorithm; function approximations; finite mixtures; RBF networks; parameter estimation N2 - The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the REBMIX algorithm, developed for the finite mixture density estimation. Since the REBMIX estimates the unknown parameters with the unique semiparametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the REBMIX algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the REBMIX algorithm can be applied successfully for the continuous function approximation. UR - https://www.sv-jme.eu/article/finite-mixture-estimation-algorithm-for-arbitrary-function-approximation/
@article{{sv-jme}{sv-jme.2011.085},
author = {Volk, M., Nagode, M., Fajdiga, M.},
title = {Finite Mixture Estimation Algorithm for Arbitrary Function Approximation},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {58},
number = {2},
year = {2012},
doi = {10.5545/sv-jme.2011.085},
url = {https://www.sv-jme.eu/article/finite-mixture-estimation-algorithm-for-arbitrary-function-approximation/}
}
TY - JOUR AU - Volk, Matej AU - Nagode, Marko AU - Fajdiga, Matija PY - 2018/06/28 TI - Finite Mixture Estimation Algorithm for Arbitrary Function Approximation JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 58, No 2 (2012): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2011.085 KW - REBMIX algorithm, function approximations, finite mixtures, RBF networks, parameter estimation N2 - The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the REBMIX algorithm, developed for the finite mixture density estimation. Since the REBMIX estimates the unknown parameters with the unique semiparametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the REBMIX algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the REBMIX algorithm can be applied successfully for the continuous function approximation. UR - https://www.sv-jme.eu/article/finite-mixture-estimation-algorithm-for-arbitrary-function-approximation/
Volk, Matej, Nagode, Marko, AND Fajdiga, Matija. "Finite Mixture Estimation Algorithm for Arbitrary Function Approximation" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 58 Number 2 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 58(2012)2, 115-124
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the REBMIX algorithm, developed for the finite mixture density estimation. Since the REBMIX estimates the unknown parameters with the unique semiparametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the REBMIX algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the REBMIX algorithm can be applied successfully for the continuous function approximation.