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Online Learning with Radial Basis Function Networks [article]

Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
2021 arXiv   pre-print
We perform feature representation transfer through clustering algorithms that determine the analytical structure of radial basis function networks we construct.  ...  These networks achieve lower mean-square prediction errors than kernel ridge regression models, which arbitrarily use all training vectors as prototypes.  ...  the radial basis function network.  ... 
arXiv:2103.08414v6 fatcat:rghnah236zbjdblodr65trr74m

The Automatic Model Selection and Variable Width RBF Neural Networks for Chaotic Time Series Prediction

Peng ZHOU, Qi YANG
2017 DEStech Transactions on Computer Science and Engineering  
This paper investigates the construction of radial basis function(RBF) neural networks, and a new self-adaptive algorithm is presented to achieve chaotic times series prediction.  ...  may guarantee a natural overlap between kernel functions.  ...  In short, BIC maybe enables us to construct a better radial basis function network nonlinear regression model than other three methods.  ... 
doi:10.12783/dtcse/cece2017/14370 fatcat:iayounqilbdm5luhamp23jnbtq

Machine Learning: A Crucial Tool for Sensor Design

Weixiang Zhao, Abhinav Bhushan, Anthony Santamaria, Melinda Simon, Cristina Davis
2008 Algorithms  
Acknowledgements This work was partially supported by several funding agencies including: grant number UL1  ...  Integrating RBFNN with multivariate analysis methods is another effective strategy for radial basis vector construction [73] .  ...  A method integrating a linear outlier detection strategy with radial basis function neural networks (RBFNN) was proposed to detect outliers in complex nonlinear systems [20] .  ... 
doi:10.3390/a1020130 pmid:20191110 pmcid:PMC2828765 fatcat:qnxufvx5djaajlqr6wsi5ltg3e

A New Approach to Radial Basis Function Approximation and Its Application to QSAR

Alexey V. Zakharov, Megan L. Peach, Markus Sitzmann, Marc C. Nicklaus
2014 Journal of Chemical Information and Modeling  
We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor's contribution.  ...  Linear radial basis functions allow one to achieve more accurate predictions for diverse data sets.  ...  We compare the RBF-SCR method with the radial basis function interpolation method and RBF neural networks with k-means clustering.  ... 
doi:10.1021/ci400704f pmid:24451033 pmcid:PMC3985791 fatcat:6pfetxpvpvd7taiqbnvqucabkq

10.5937/sjm9-5520 = On robust information extraction from high-dimensional data

Jan Kalina
2014 Serbian Journal of Management  
Acknowledgements The work was supported by the Czech Science Foundation project No. 13-01930S (Robust methods for nonstandard situations, their diagnostics and implementations).  ...  Radial Basis Function Network A radial basis function network is able to model a continuous nonlinear function.  ...  basis function networks, self-organizing maps, and support vector machines.  ... 
doi:10.5937/sjm9-5520 fatcat:vuzzfbopnnchddwxslzla3xxw4

An Enhanced Support Vector Regression Model for Weather Forecasting

R. Usha Rani
2013 IOSR Journal of Computer Engineering  
An attempt is made in this paper to develop an Enhanced Support Vector Regression (ESVR ) model with more un-interpretable kernel functions in the domain of forecasting the weather conditions.  ...  ) classification, SVM got much more esteemed performance in forecasting any one parameter with respect to others in a two stage procedure initial with self organizing maps and with best practice of more  ...  Various techniques like linear regression, auto regression, Multi Layer Perceptron, Radial Basis Function networks are applied to predict atmospheric parameters like temperature, wind speed, rainfall,  ... 
doi:10.9790/0661-1222124 fatcat:obkhnu77cnbira4vr6hv5vtgqm

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya, Devang Odedra, Subir Samanta, Ambarish S. Vidyarthi
2010 The Review of Diabetic Studies  
The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays.  ...  A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory.  ...  RBF networks have the disadvantage of requiring good coverage of the input space by radial basis functions.  ... 
doi:10.1900/rds.2010.7.252 pmid:21713313 pmcid:PMC3143540 fatcat:p2dvf7jiurd6bo42sbsapse7pq

Guest Editors' Introduction: Neural Networks For Signal Processing

A.G. Constantinides, S. Haykin, Yu Hen Hu, Jenq-neng Hwang, S. Katagiri, Sun-yuan Kung, T.A. Poggio
1997 IEEE Transactions on Signal Processing  
In "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers," an experimental comparison of a support vector (SV) learning algorithm to the k-means clustering algorithm  ...  for the training of radial basis networks is presented.  ... 
doi:10.1109/tsp.1997.650089 fatcat:se3mm23wzna73iwb6to666b2re

Statistical Methods and Artificial Neural Networks

Mammadagha Mammadov, Berna Yazici, Şenay Yolaçan, Atilla Aslanargun, Ali Fuat Yüzer, Embiya Ağaoğlu
2005 Journal of Modern Applied Statistical Methods  
Key words: Time series, ARIMA, neural networks, hybrid models, logistic and probit regression, rescheduling and non-rescheduling of the international debts, Kohonen nets, cluster analysis, Maastricht criteria  ...  Artificial Neural Networks and statistical methods are applied on real data sets for forecasting, classification, and clustering problems.  ...  Radial Basis Function Networks (RBFN) RBFN (Haykin, 1999; Bishop, 1995) are also used, aside from MLP networks, in regression and classification problems.  ... 
doi:10.22237/jmasm/1162354980 fatcat:3gahwbs6rnbazkslvmuxhri334

Credit evaluation model of loan proposals for Indian Banks

Seema Purohit, Anjali Kulkarni
2011 2011 World Congress on Information and Communication Technologies  
The integrated model is a combination model based on the techniques of Logistic Regression, Multilayer Perceptron Model, Radial Basis Neural Network, Support Vector Machine and Decision tree (C4.5) and  ...  N Kulkarni specially thanks Bank officials of many banks in Maharashtra, who helped by providing the data needed for the study and the management of NMITD for motivating at every stage of this study.  ...  Objective of the study is to find the Integrated model which can be constructed by combining the advantages of Radial Basis neural network, Multilayer Perceptron Model, Logistic regression, SVM and decision  ... 
doi:10.1109/wict.2011.6141362 fatcat:7x6k4ndbnrd7lculfacezbwo4m

Pattern analysis for machine olfaction: a review

R. Gutierrez-Osuna
2002 IEEE Sensors Journal  
, clustering, and validation.  ...  A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics has been used to process electronic nose data.  ...  Radial Basis Function Classifiers Radial basis functions (RBFs) are feed-forward connectionist architectures consisting of a hidden layer of radial kernels and an output layer or linear neurons [54] .  ... 
doi:10.1109/jsen.2002.800688 fatcat:vcfjqcdndreotfdhcvjz3btyti

Provincial Grid Investment Scale Forecasting Based on MLR and RBF Neural Network

Ersheng Pan, Dong Peng, Wangcheng Long, Yawei Xue, Lang Zhao, Jinchao Li
2019 Mathematical Problems in Engineering  
, installed power capacity of operation area, maximum power load, and other growth rates by using the multiple linear regression method (MLR), and the estimation error is corrected by using RBF neural  ...  network.  ...  Acknowledgments This work has been supported by National Key R&D Program of China (2016YFB0900100).  ... 
doi:10.1155/2019/3197595 fatcat:ws5zkzbdfvcxtcfbtuwufqf3oi

EYE DETECTION USING OPTIMAL WAVELET PACKETS AND RADIAL BASIS FUNCTIONS (RBFs)

JEFFREY HUANG, HARRY WECHSLER
1999 International journal of pattern recognition and artificial intelligence  
This paper introduces a novel approach for the eye detection task using optimal wavelet packets for eye representation and Radial Basis Functions (RBFs) for subsequent classification ('labeling') of facial  ...  Entropy minimization is thus functionally compatible with the 1st operational stage of the RBF classifier, that of clustering, and this explains the improved RBF performance on eye detection.  ...  Acknowledgments: This work was partially supported by the U.S. Army Research Laboratory under contract DAAL01-97-K-0118.  ... 
doi:10.1142/s0218001499000562 fatcat:llib3diwsncz7expouybwqasxq

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Yu Wang, Gengxin Sun
2022 Mathematical Problems in Engineering  
Aiming at the problems of the radial basis network model, this paper proposes a multimedia vocal singing automation evaluation network model, combined with the characteristics of multimedia modeling innovation  ...  First of all, the theory and algorithm of analytic hierarchy process and radial basis function network are researched and analyzed, and the RBF is predicted for the mature area of multimedia development  ...  Finally, Table 1 uses partial least squares regression, RBF network, and radial basis function to establish prediction models, with R, RMSEP, and RPD as the models.  ... 
doi:10.1155/2022/3868389 fatcat:w25i6sys6jcz7fvprjxxapuwim

Predicting construction labor productivity using lower upper decomposition radial base function neural network

Sasan Golnaraghi, Osama Moselhi, Sabah Alkass, Zahra Zangenehmadar
2020 Engineering Reports  
The predictive capability of the developed model is then compared with other techniques including adaptive neuro-fuzzy inference system, artificial neural network, radial basis function (RBF), and generalized  ...  regression neural network.  ...  and the task of regression, the radial basis function (RBF) was deemed suitable NN type for mode development.  ... 
doi:10.1002/eng2.12107 fatcat:ny2uouz2hzaq7agvh2mw2k3xga
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