Sentence examples for topic distribution 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.

Moreover, the Dirichlet prior to the per-document topic distribution significantly reduces the effect overfitting.

TPR resorts to Wikipedia articles to train a topic distribution for terms to assist keyphrase extraction.

The cumulative topic distribution in Figure 6 includes only topic proportions greater than 0.02.

The ART model [85] describes the per message topic distribution based on author and recipient pair.

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

They focus on inference of social influence from topic distributions or joint inference of influence diffusion and topic distributions.

We use topic distributions to measure clustering among formally recognized crime types.

A few studies look into the interplay of social influence and topic distributions [14, 20 22].

Then, the model fitting involves the estimation of topic specific visual word distributions and image specific latent topic distributions from the given database using Maximum Likelihood Estimation (MLE).

This could bring more insights to the interplay between topic distributions and influence diffusion, which could guide future algorithm design.