Sentence examples for model selection at from inspiring English sources

In this paper, we propose a new approach called ensemble active learning for solving the problems of active learning and model selection at the same time.

Regional interactions between left and right ventral and dorsal frontal regions were assessed using dynamic causal modeling (DCM), random-effects Bayesian model selection at the family level, and Bayesian model averaging at the connection level.

In this study we used the F3X4 and F61 models of codon frequencies [ 51], as well as the recently developed FMutSel model [ 32], which models mutational biases and introduces a parameter of codon fitness to model selection at silent sites.

A more promising and efficient direction would be to consider model selection at the task level where only the most relevant and useful tasks are used for multitask learning.

Proportional hazard regression containing all clinical and pathological variables found pCR, cN3, and gender as independent prognostic factors for survival using backward elimination of non-significant variables for reduced model selection at a significance level of α = 0.05 (model (1), Table  3).> -wrap-foot> Model (1) contains all clinical and pathological covariates, as described under Methods.

In study 1 (discovery analysis), following Motzer et al (1999, 2002) and Heng et al (2009), baseline factors were first individually evaluated for association with OS using a univariate Cox proportional hazards model, and subsequently evaluated using multivariate stepwise model selection (forward selection at P≤0.1 to enter the model and backward selection at P≤0.05 to stay in the model) (Table 2).

The orders employed in the oracle method are obtained using the original signal f. Figure 1 Comparison of the performance of different model selection methods at SNR  = 10 to 20 dB for: (a) piecewise polynomial signal f ( x ), and (b) Heavisine signal.

The 'Model Selection tool' at the online HyphY server (see methods) identified the HKY85 model as the most appropriate using the AIC criterion (p <0.07).

And it was also estimated via the automatic model selection tool at the Datamonkey website (http://www.datamonkey.org) for the coding regions [ 43, 44].

Of the seven [of 29; see Supplemental Material, Table S1 (online at http://www.ehponline.org/members/2008/10497/suppl.pdf)] other variables selected into the models, none was another pollutant (Table 3) [see Supplemental Material for details of model selection (online at http://www.ehponline.org/members/2008/10497/suppl.pdf)].

This work presents an improved hybrid approach to solve the feature model selection problem, aiming at supporting product portfolio scoping.