Discriminative model

Discriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sick. Typical discriminative models include logistic regression (LR), conditional random fields (CRFs) (specified over an undirected graph), decision trees, and many others. Typical generative model approaches include naive Bayes classifiers, Gaussian mixture models, variational autoencoders, generative adversarial networks and others. Unlike generative modelling, which studies the joint probability , discriminative modeling studies the or maps the given unobserved variable (target) to a class label dependent on the observed variables (training samples). For example, in object recognition, is likely to be a vector of raw pixels (or features extracted from the raw pixels of the image). Within a probabilistic framework, this is done by modeling the conditional probability distribution , which can be used for predicting from . Note that there is still distinction between the conditional model and the discriminative model, though more often they are simply categorised as discriminative model. A conditional model models the conditional probability distribution, while the traditional discriminative model aims to optimize on mapping the input around the most similar trained samples. The following approach is based on the assumption that it is given the training data-set , where is the corresponding output for the input . We intend to use the function to simulate the behavior of what we observed from the training data-set by the linear classifier method. Using the joint feature vector , the decision function is defined as: According to Memisevic's interpretation, , which is also , computes a score which measures the compatibility of the input with the potential output . Then the determines the class with the highest score.