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34 lines
1.9 KiB
34 lines
1.9 KiB
.. _Bayes Classifier: |
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Normal Bayes Classifier |
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======================= |
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.. highlight:: cpp |
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This simple classification model assumes that feature vectors from each class are normally distributed (though, not necessarily independently distributed). So, the whole data distribution function is assumed to be a Gaussian mixture, one component per class. Using the training data the algorithm estimates mean vectors and covariance matrices for every class, and then it uses them for prediction. |
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.. [Fukunaga90] K. Fukunaga. *Introduction to Statistical Pattern Recognition*. second ed., New York: Academic Press, 1990. |
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NormalBayesClassifier |
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.. ocv:class:: NormalBayesClassifier : public StatModel |
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Bayes classifier for normally distributed data. |
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NormalBayesClassifier::create |
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Creates empty model |
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.. ocv:function:: Ptr<NormalBayesClassifier> NormalBayesClassifier::create(const NormalBayesClassifier::Params& params=Params()) |
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:param params: The model parameters. There is none so far, the structure is used as a placeholder for possible extensions. |
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Use ``StatModel::train`` to train the model, ``StatModel::train<NormalBayesClassifier>(traindata, params)`` to create and train the model, ``StatModel::load<NormalBayesClassifier>(filename)`` to load the pre-trained model. |
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NormalBayesClassifier::predictProb |
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Predicts the response for sample(s). |
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.. ocv:function:: float NormalBayesClassifier::predictProb( InputArray inputs, OutputArray outputs, OutputArray outputProbs, int flags=0 ) const |
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The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``inputs``. In case of multiple input vectors, there should be one output vector ``outputs``. The predicted class for a single input vector is returned by the method. The vector ``outputProbs`` contains the output probabilities corresponding to each element of ``result``.
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