Sentence examples for distribution probabilities from inspiring English sources

Instead of using Fisher similarity function for categorization using topic distribution probabilities, AdaBoost is employed to build a robust classifier using PLSA topic distribution probabilities as feature.

Using labeled emails as input dataset, containing phishing emails and non-phishing emails, topic distribution probabilities and word distribution probabilities are obtained by building a PLSA model.

The features to build the classifier are the topic distribution probabilities obtained from the PLSA model.

For both views, once the PLSA model is built, the topic distribution probabilities are extracted as features.

The PLSA topics are discovered as before in phishGILLNET1 and topic distribution probabilities on training data are estimated.

The topic distribution probabilities on the test set is derived using PLSA fold-in (see Section 4).

To model heterogeneity in the populations of defects we assumed a log- normal distribution probability function.

The isosurface of the e-h distribution probability is shown in green-yellow (color online).

The P-Value is calculated using binomial distribution probability.

Exact confidence intervals were calculated using binominal distribution probability.

The distribution probability distributions are linked to the degree of specialization or flexibility of the given network topology.