Bayesian regularization
Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training
... applying Bayesian regularization to improve Levenberg-Marquardt (LM) algorithm and make it better usable in training neural ...using Bayesian regularization and use it to determine an ...
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Konrath, Susanne (2013): Bayesian regularization in regression models for survival data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... features, regularization methods are utilized that shrink the regression coefficient estimates toward zero and simultaneously enforce some coefficients to be set equal to zero, which are then interpreted as ...
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An Application of ANN Model with Bayesian Regularization Learning Algorithm for Computing the Operating Frequency of C-Shaped Patch Antennas
... In this study, a method of feed forward back propagation (FFBP) ANN model based on multilayered perceptron (MLP) has been designed to compute the operating frequencies of CPAs. In order to create a population data for ...
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Microwave Characterization of Dielectric Materials Using Bayesian Neural Networks
... Abstract—This paper shows the efficiency of neural networks (NN), coupled with the finite element method (FEM), to evaluate the broad- band properties of dielectric materials. A characterization protocol is built to ...
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A NEW SOFT SET BASED PRUNING ALGORITHM FOR ENSEMBLE METHOD
... and Bayesian Regularization methods and compare the results of these ...variable. Bayesian regularization is a statistical model which process nonlinear ...errors. Bayesian ...
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Indian stock market prediction using artificial neural networks on tick data
... and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results ...and Bayesian Regularization respectively which is ...
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A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks
... NAR methods are efficiently used for forecasting as deterministic models, as well as stochastic models. In this study, we consider a three-layer neural network, in which the feed-forward algorithm (1), (2) is learned ...
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Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis
... using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis ...
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GROUNDWATER LEVEL SIMULATION USING ANN FOR GANDHINAGAR DISTRICT
... Abstract- In this research, Artificial Neural Network is used to predict groundwater level in Gandhinagar district. Model considers precipitation, maximum and minimum temperature, humidity, and Evaporation data as input ...
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An Efficient Neural Network Based System for Diagnosis of Breast Cancer
... The designed 10-(5-5)-1 neural network is trained using two variants of Back propagation algorithm, namely scaled conjugate gradient (SCG) and Bayesian regularization (BR). The SCG method was used to train ...
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Locally Regularized Smoothing B-Snake
... cal regularization is ...local regularization involves a matrix inversion step at each ...local regularization and has a strong initialization- dependent minimization process linked to the ...
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Kernels: Regularization and Optimization
... Since the kernel has to effectively capture the domain knowledge in an application, we study the problem of learning the kernel itself from training data. The proposed solution is a kernel on the space of kernels itself, ...
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New Regularization Algorithms for Solving the Deconvolution Problem in Well Test Data Interpretation
... Finally, Cinar et al. [28] compared efficiency and ro- bustness of pressure-rate deconvolution algorithms of von Schroeter et al. [18,19], Levitan [20], and Ilk et al. [25]. First, they stated that the ...
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A View of Margin Losses as Regularizers of Probability Estimates
... The data sets used in the previous section are of relatively small size. To investigate the ben- efits of loss regularization for larger data sets, we considered the ADULT, LETTER.p1 and LETTER.p2 data sets, which ...
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Regularization of ill posed mixed variational inequalities with non monotone perturbations
... a regularization method for ill-posed mixed variational inequalities with non-monotone perturbations in Banach ...posteriori regularization parameter choice that is based upon the generalized discrepancy ...
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Nonparametric Sparsity and Regularization
... In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a ...
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Improved hydrogeophysical characterization using joint inversion of cross hole electrical resistance and ground penetrating radar traveltime data
... define regularization operators that are consistent with available borehole data and geological understanding; thereby, constructing models of physical properties that are more closely related to the underlying ...
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Density Driven Cross Lingual Transfer of Dependency Parsers
... terior regularization (Ganchev et al., 2009), the use of entropy regularization and parallel guid- ance (Ma and Xia, 2014), the use of a simple method to transfer delexicalized parsers across languages ...
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Expanding the applicability of Lavrentiev regularization methods for ill-posed problems
... 18. Argyros, IK: A semilocal convergence for directional Newton methods. Math. Comput. 80, 327-343 (2011) 19. Argyros, IK, Hilout, S: Weaker conditions for the convergence of Newton’s method. J. Complex. 28, 364-387 ...
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Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System
... Tikhonov regularization method is one of the best method to solve the ill-posed problem, but in ECT it tends to generate a smooth approximation solution, in that case it can lead the lost detailed information ...
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