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The design idea of ComTSP is, if the error made by the subensemble for a pruning sample is larger than that by the candidate predictor to a certain extent, it is assumed that the predictor is complementary to the subensemble.
The predictor avoids over-fitting the predictor to a specific sequence, i.e in deterministic settings, a predictor that is applicable to different sets of sequences [20,37].
This shows that B and C stem from different data-collection timelines, and comparing them is equivalent to comparing an online batched predictor to a "prescient" one, which has access to all the data at once.
For example, a target matching method has been proposed in [13]: The authors match the output of the LPC predictor to a target signal constructed using a smoothed version of the excitation signal, in order to jointly smooth both the residual signal and the frame-to-frame variation of LSF coefficients.
Comparing a logistic regression model with gender included as a predictor to a constant-only model was statistically significant, χ2 (1, 839) = 5.389, p<.05.
A logistic regression revealed political affiliation to be a significant predictor of people's crime-fighting suggestions: comparing a model with political affiliation included as a predictor to a constant-only model was statistically significant, χ2 (1, 839) = 3.98, p<.05.
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Growth faults terminate upwards at the base of volcaniclastic units, thus representing a predictor to an eruption phase.
Conceptually, universal prediction compares the designed predictor to an indexed set (mathbb {M}) of stochastic sources (e.g. distributions, codes, or polynomials).
The proposed stepwise and lasso regressions reduce the number of predictors to a few.
McFadden R and deviance compare the final model (with all predictors) to a fully saturated model.
This regression method reduces the number of predictors to a minimum set, providing a clear picture of the kinases involved in the response of cell lines.
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