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  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. [3] All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.

  3. Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Bayes_classifier

    In statistical classification, the Bayes classifier is the classifier having the smallest probability of misclassification of all classifiers using the same set of features. [ 1 ] Definition

  4. Ensemble learning - Wikipedia

    en.wikipedia.org/wiki/Ensemble_learning

    The Bayes optimal classifier is a classification technique. It is an ensemble of all the hypotheses in the hypothesis space. On average, no other ensemble can outperform it. [18] The Naive Bayes classifier is a version of this that assumes that the data is conditionally independent on the class and makes the computation more feasible. Each ...

  5. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    Naive Bayes is a successful classifier based upon the principle of maximum a posteriori (MAP). This approach is naturally extensible to the case of having more than two classes, and was shown to perform well in spite of the underlying simplifying assumption of conditional independence .

  6. Naive Bayes spam filtering - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_spam_filtering

    A simple solution is to simply avoid taking such unreliable words into account as well. Applying again Bayes' theorem, and assuming the classification between spam and ham of the emails containing a given word ("replica") is a random variable with beta distribution, some programs decide to use a corrected probability:

  7. Bayes' theorem - Wikipedia

    en.wikipedia.org/wiki/Bayes'_theorem

    Bayes' theorem is named after the Reverend Thomas Bayes (/ b eɪ z /), also a statistician and philosopher. Bayes used conditional probability to provide an algorithm (his Proposition 9) that uses evidence to calculate limits on an unknown parameter. His work was published in 1763 as An Essay Towards Solving a Problem in the Doctrine of Chances.

  8. Bag-of-words model in computer vision - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model_in...

    The simplest one is Naive Bayes classifier. [2] Using the language of graphical models, the Naive Bayes classifier is described by the equation below. The basic idea (or assumption) of this model is that each category has its own distribution over the codebooks, and that the distributions of each category are observably different.

  9. The Most Incompatible Zodiac Signs That Should Never, Ever ...

    www.aol.com/most-incompatible-zodiac-signs-never...

    Olena Ruban/Getty Images. 4. Incompatible: Taurus and Aquarius. The zodiac’s bull is stable and predictable, especially in matters of the heart.That can spell romance and reliability for some ...