M.C. Denham, Choosing the number of factors in partial least squares regression: estimating and minimizing the mean squared error of prediction, J. Chemom, 14 (2000) 351-361.Denham MC. Choosing the number of factors in partial least squares regression: estimating and minimizing the mean ...
Ïn this paper, the authors have established the design criteria by which mean-squared-error value is minimized. Though they are rough, we can apply them effectively for the design of compensator.When noise is present, Wiener's theory of Optimum Filter is applied, and when noise is negligib...
Minimizing the squared mean curvature integral for surfaces in space forms. Exp Math 1992;1(3): 191-207.L. Hsu,R. Kusner, J. Sullivan, Minimizing the squared mean curvature integral for surfaces in space forms, Experiment. Math. 1 (1992), no. 3, 191207....
PTQ also does not require a comprehensive understanding of the model, and thus has been attracting attention recently. Early works on post-training quantization are concen- trated on minimizing the quantization error which is defined as the mean squared ...
This paper proposes a method to optimize the performance of tandem source-channel coding with respect to the mean-squared error by exploiting the unequal error protection coding. More specifically, we formulate a combination of linear programming and grid search to optimize degree distributions for une...
In: from scipy.optimize import fmin xopt = fmin(squared_cost, x0=0, xtol=1e-8, args=(x,)) Out: Optimization terminated successfully. Current function value: 7.300000 Iterations: 44 Function evaluations: 88 We just output our best e value and verify if it actually is the mean of the ...
As is well known, the predictor coefficients are obtained by minimizing the mean‐squared prediction error over a speech segment typically 10 to 20 msec lo... Atal,S B. - 《Journal of the Acoustical Society of America》 被引量: 238发表: 1974年 Apparatus and methods for cooling semiconductor...
The optimal MinMax classifier hOPT is the mean of the leftmost blue point and the rightmost red point and can be computed in Θ(n) time. Indeed, by definition, the cost s∞(h) is realized by the misclassified points furthest from h and thus can be reduced by a small change of h in...
The mean squared error of the M covariates weighted according to Eq.3 is used to calculate the bias loss: $$ {\mathcal{L}}_{BIAS}=\frac{1}{M}\sum \limits_{j=1}^M{\left(\frac{\sum \limits_i{t}^{(i)}{w}^{(i)}{x}_j^{(i)}}{\sum \limits_i{t}^{(i)}{w}^{(...
However, most practical quantization schemes use simpler error measures, such as mean squared error (MSE) or weighted mean squared error (WMSE) measures between the quantized and unquantized LPC coefficients, reflection coefficients, arcsine coefficients, area ratios, or line spectral pair frequencies ...