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I am doing a project that has some Natural Language Processing to do. I am using stanford MaxEnt Classifier for the purpose.But I am not sure, whether Maximum entropy model and logistic regression are one at the same or is it some special kind of logistic regression?
Can anyone come up with an explanation?
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This is exactly the same model. NLP society prefers the name Maximum Entropy and uses the sparse formulation which allows to compute everything without direct projection to the R^n space (as it is common for NLP to have huge amount of features and very sparse vectors). Save this answer.
Maximum entropy modeling (MaxEnt) uses techniques developed from machine learning, allowing empirical data to be used to predict the probability of finding something under certain conditions distributed in space (Dudík et al. 2007). MaxEnt uses presence only data by generating random test points.
There are three main types of logistic regression: binary, multinomial and ordinal.
Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers.
This is exactly the same model. NLP society prefers the name Maximum Entropy and uses the sparse formulation which allows to compute everything without direct projection to the R^n space (as it is common for NLP to have huge amount of features and very sparse vectors).
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