Regression, Logistic Regression and Maximum Entropy

For classification tasks there are three widely used algorithms; the Naive Bayes, Logistic Regression / Maximum Entropy and Support Vector Machines. We have already seen how the Naive Bayes works in the context of Sentiment Analysis. Although it is more accurate than a bag-of-words model, it has the assumption of conditional independence of its features. This is a simplification which makes the NB classifier easy to implement, but it is also unrealistic in most cases and leads to a lower accuracy. A direct improvement on the N.B. classifier, is an algorithm which does not assume conditional independence but tries to estimate the weight vectors (feature values) directly.