In the last lecture we discussed the relationships between different modeling paradigms such as the Bayesian approach, Maximum A Posteriori (MAP) approach, Maximum Likelihood (ML) approach, and the Leastsquares (LS) method. In this lecture we first prove that equivalence of LS and ML under the assumption of normally distributed error. Then, the notions of the naive Bayesian classifier and the L...

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman propo...