Sentence examples for mixture of topics from inspiring English sources

The main idea behind LDA assumes that the words of each document arise from a mixture of topics, each of which is a multinomial distribution over the vocabulary.

Topic models annotate images as the samples from a specific mixture of topics, where each topic is a distribution over image observations.

The above lemma suggests that we can use the marginal influence of a node on each topic when dealing with a mixture of topics.

Latent topic models are based on the assumption that a text document can be represented as a mixture of topics, and each topic can be represented as a mixture over a dictionary of words.

Recently, Barbieri et al. [16] propose the topic-aware independent cascade (TIC) and linear threshold (TLT) models, in which a diffusion item is a mixture of topics and influence parameters for each item are also mixtures of parameters for individual topics.

Erlewine found that the songs on the album had a poignant mixture of topics, which further solidied its popularity.

Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics.

Here a confusion matrix is used to illustrate both how official crime types exist as mixtures of topics and how individual topics are associated with many different official crime types.

Finally, we outline cosine similarity as and average linkage clustering for measuring the distance between official recognized crime types (e.g., robbery, burglary, assault) based on the mixtures of topics represented by those events.

However, topic modeling considers samples as a mixture of latent topics and each topic is characterized by the probabilistic distribution of genes.

The basic idea is that a document is represented as a mixture of several topics, where each topic is characterized by a word distribution.