and Mean squared error.
Convex vs Non-Convex Estimators for Regression and Sparse Estimation: the Mean Squared Error Properties of ARD and GLasso
... We study a simple linear regression problem for grouped variables; we are interested in methods which jointly perform estimation and variable selection, that is, that automatically set to zero groups of variables in the ...
36
An Empirical Study on Mushroom Disease Diagnosis:A Data Mining Approach
... of error rate ...of error rates namely Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared ...
6
Investigation of the Performance of different Spatial Filters toward Mammogram De-noising
... namely Mean, Median, Wiener and Gaussian filters were employed and the processed images were evaluated for Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Correlation ...
5
A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking
... the mean-squared-error cost function with a term that represents the risk of a track swap, and applying the same basic algorithm to demonstrate how the guidance algorithm reduces the probability of a ...
17
Adaptive digital predistortion linearisation for RF power amplifiers
... Development of linear modulation schemes has opened the way for spectrally efficient, high speed digital communication systems for voice and data applications. A trend has been to develop utra wide and wide bandwidth ...
5
Two-stage estimation to adjust for treatment switching in randomised trials: A simulation study investigating the use of inverse probability weighting instead of re-censoring
... the mean estimated RMST and the true RMST and expressing this as a percentage of true RMST ...root mean squared error (RMSE) of the percentage bias was calculated to provide information on the ...
20
Antenna arrays in multi user detection of spread spectrum signals
... frame error rates to be unacceptably ...the error rates increase with the number of ...minimum mean-squared error due to MAI, and show how the frame error rate increases with the ...
6
Fault Classification with Artificial Neural Networks based on the Application of Teager Energy Operator
... In [3] current signals are used as input parameters in ANN training using Back Propagation algorithm for diagnosis of the fault in a distributed motor network. Different number of nodes where tested in network structure ...
6
Rates of convergence of some estimators in a class of deconvolution problems
... Key Words and Phrases: Deconvolution, density estimation, mean squared error, measurement error, rates of convergence, uniform convergence.. 1 Work supported by NSF Grant DMS-86-13681A[r] ...
10
A comparison of robust estimators in simple linear regression
... The bias and mean squared error (MSE) of the various estimators of slope, intercept, and mean response were estimated and compared in a simulation study.. All computing was done in Fortr[r] ...
22
Bayesian forecasting of mortality rates by using latent Gaussian models
... the mean width of the prediction intervals and the mean interval ...the mean forecasts was assessed by using the root-mean-squared error of the predicted ...The mean width ...
23
Soil Thermal Diffusivity of a Gleyic Solonetz Soil Estimated by Different Methods in the Brazilian Pantanal
... Using the soil thermal diffusivity estimated by ampli- tude method as reference was computed mean absolute error-MAE and the root mean squared error-RMSE be- tween phase methods, ...
8
Forecasting Spare Parts Demand Using Statistical Analysis
... least error value were ...the mean error (ME), the mean squared error (MSE) and mean absolute deviation ...root mean squared error (RMSE) is used ...
8
Enhancement of Fuzzy Possibilistic C Means Algorithm using EM Algorithm (EMFPCM)
... The major difficulties that arise in several fields, comprising pattern recognition, machine learning and statistics, is clustering. The basic data clustering problem might be defined as finding out groups in data or ...
6
Improved generalized family of estimators of population mean using information on transformed auxiliary variables
... population mean Y of the study variable y using information on transformed auxiliary ...and mean squared error (MSE) of the suggested classes of estimators to the first degree of ...minimum ...
22
Title: Biometric Authentication of an Individual Using Multilayer Perceptron and Support Vector Machine
... Abstract— The proposed method uses multi layered perceptron neural network and support vector machine to classify the normal subjects. Data used for training, testing and cross validation was recorded from normal persons ...
5
Performance Enhancement for Coexistence Adaptive Interference Draining using Rectangular Antenna’s for MUE’s
... minimum mean squared error (MMSE) interference in Rectangular antennas which will improves the performance of femtocell users, by maintaining the quality of the communication channel of macro-cell ...
7
Choosing the group size for group testing to estimate a proportion
... The group size obtained by equating the absolute value of the influence of a group showing the trait with one not showing the trait provides an estimate with a smaller mean squared error[r] ...
33
Variable marginal propensities to pirate and the diffusion of computer software
... the mean squared error for our model with the word processor data is the same as the restricted model, whereas it is much lower with the spreadsheet ...
28
Improve the Active Subspace Method by Partitioning the Parameter Space
... the squared error as a crite- ...lower mean squared error on separately generated test points) than the response surface created by the original active subspace ...
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