Regularized Logistic Regression

In this section we describe in detail the multinomial logistic regression classification models we propose for the text classification problem. Models are defined as maximum a posteriori (MAP) point estimates based on Laplace priors (equivalently $L_1$ regularization). The parameters were estimated from fully labeled training data using the online learning method of stochastic gradient descent run until convergence.