Sentence examples for approaches naive from inspiring English sources

Both approaches, naive or taking right truncation into account, were compared with a simulation study.

For the identification of "positive" (diversifying) selection, we used model M8 with two Bayesian approaches (Naive Empirical Bayesian analysis and Bayes Empirical Bayesian or BEB) (Yang et al. 2005).

They also show that the proposed approach outperforms Ant-Tree-Miner (an evolutionary DT construction approach), Naive Bayes and Support Vector Machine in terms of accuracy and F-measure.

To test this, we used a machine learning approach (Naive Bayes Simple) with feature selection on a training data set and then applied this to a test set.

For a finite sample size, the simulations show that, whatever the approach, naive or truncation-based, the parametric maximum likelihood estimator may be positively biased and that this bias and the corresponding mean squared error increase when the theoretical probability p for the time-to-onset to fall within the observable values interval decreases.

Almost all existing feature weighting approaches for naive Bayes text classifiers have some defects: limited improvement to classification performance of naive Bayes text classifiers or sacrificing the simplicity and execution time of the final models.

As a result, this paper proposes two adaptive feature weighting approaches for naive Bayes text classifiers.

This paper works on feature weighting approaches for naive Bayes text classifiers.

However, such approaches implement naive topology-based inference and do not take into account important features within the drug target domain.

Yet such an approach is naive at best.

We compare this approach with naive and vectorized implementations for CPUs.