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Lasso applies linear discriminant analysis with minimizing sum of regression coefficients.
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We use regression analysis assuming normally distributed errors (minimizing sums of squares) and Poisson distributed errors (maximizing Poisson log-likelihood).
The fit for each model to observed counts of observed waterbirds is performed using regression analysis, minimizing sums of squares by assuming both normally distributed and log-normally distributed errors.
Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters.
A firm wants to minimize sum of discounted total cost of satisfying demands for a single input.
The main objective of Function (12) is to minimize sum of the total transportation cost for the vehicles of coalition S m.
The method finds the optimal design of the vehicle by iteratively using dynamic programming (DP) and convex optimization to minimize sum of operational and component costs over a given driving cycle.
A new method is proposed here that minimizes sum of the total transportation cost for the vehicles of a coalition.
Three objectives are considered: minimizing the sum of squares of distances, minimizing the maximum distance, and minimizing the sum of distances.
Kinetic and transport parameters are estimated by minimizing the sum-of-squares error between the model and experimental measurements using a simplex method.
The model was calibrated to fit the initial epidemic curve observed in Ontario (by minimizing the sum-of-squares difference between model projections and the observed epidemic curve).
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