Summary of Supervised Learning Methods

This chapter summarizes the characteristics of ten supervised learning methods, including the perceptron, k-Nearest-Neighbor (k-NN), the Naïve Bayes method, the decision tree, logistic regression and maximum entropy model, Support Vector Machine (SVM), Boosting, the EM algorithm, Hidden Markov Model (HMM), and Conditional random field (CRF).