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A perceptron is a linear threshold function. That means it has a weight vector $w$, and it outputs $w \cdot x > t$, where $x$ is the input vector and $t$ the threshold. Naïve Bayes makes the assumption that all features are independent (hence the term naïve). It predicts the most likely class by using Bayesian probability, for each class multiplying the ...


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Naive Bayes is a generative algorithm while Perceptron is a discriminative algorithm. That is the main difference.


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