naive bayes classifier造句

• The naive Bayes classifier combines this model with a decision rule.
• Decision trees, neural networks, or a naive Bayes classifier could be used.
• Spam classification is treated in more detail in the article on the naive Bayes classifier.
• The multinomial naive Bayes classifier becomes a linear classifier when expressed in log-space:
• Spam filtering software based on this formula is sometimes referred to as a naive Bayes classifier.
• How does a naive Bayes classifier change if some of the classes are not mutually exclusive?
• In 2015, Zhou et al . suggested to apply naive Bayes classifier to detect pathological brains.
• Other reasons for the observed success of the naive Bayes classifier are discussed in the literature cited below.
• For some types of probability models, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.
• The assumptions on distributions of features are called the " event model " of the Naive Bayes classifier.
• A naive Bayes classifier will assume the pixels are statistically independent random variables and therefore fail to produce good results.
• Despite their naive design and apparently oversimplified assumptions, naive Bayes classifiers have worked quite well in many complex real-world situations.
• Besided, features like data classification through statistical analysis of trained sentences and native language processor integrated Naive Bayes classifier are claimed to be inspiring.
• The single results are then assessed by a Naive Bayes classifier which decides whether or not their combined effect might be deleterious for the protein.
• In 2004, an analysis of the Bayesian classification problem showed that there are sound theoretical reasons for the apparently implausible efficacy of naive Bayes classifiers.
• Note that a naive Bayes classifier with a Bernoulli event model is not the same as a multinomial NB classifier with frequency counts truncated to one.
• Despite the fact that the far-reaching independence assumptions are often inaccurate, the naive Bayes classifier has several properties that make it surprisingly useful in practice.
• For example, the naive Bayes classifier will make the correct MAP decision rule classification so long as the correct class is more probable than any other class.
• The softmax function is used in various multiclass classification methods, such as multinomial logistic regression, multiclass linear discriminant analysis, naive Bayes classifiers, and artificial neural networks.
• If the data are first encoded in a factorial way, however, then the naive Bayes classifier will achieve its optimal performance ( compare Schmidhuber et al . 1996 ).