naive bayes classifier造句

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 ).
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While the specific detection methods used by these companies are often proprietary, Pearson's chi-squared test and stochastic characterization with Naive Bayes classifiers are two approaches that were published in 2007.
The site analyzes users'writing samples and, by looking for certain keywords, vocabulary, and style via a naive Bayes classifier returns the name of a popular writer the sample most closely resembles.
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables ( features / predictors ) in a learning problem . iterative approximation as used for many other types of classifiers.
This formula defines a special form of One Dependence Estimator ( ODE ), a variant of the naive Bayes classifier that makes the above independence assumption that is weaker ( and hence potentially less harmful ) than the naive Bayes'independence assumption.
Naive Bayes classifiers work by correlating the use of tokens ( typically words, or sometimes other things ), with spam and non-spam e-mails and then using Bayes'theorem to calculate a probability that an email is or is not spam.
Rennie " et al . " discuss problems with the multinomial assumption in the context of document classification and possible ways to alleviate those problems, including the use of tf idf weights instead of raw term frequencies and document length normalization, to produce a naive Bayes classifier that is competitive with support vector machines.

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