Margin-infused relaxed algorithm (MIRA)[1] is a machine learning algorithm, an online algorithm for multiclass classification problems.
It is designed to learn a set of parameters (vector or matrix) by processing all the given training examples one-by-one and updating the parameters according to each training example, so that the current training example is classified correctly with a margin against incorrect classifications at least as large as their loss.
[2] The change of the parameters is kept as small as possible.
A two-class version called binary MIRA[1] simplifies the algorithm by not requiring the solution of a quadratic programming problem (see below).
When used in a one-vs-all configuration, binary MIRA can be extended to a multiclass learner that approximates full MIRA, but may be faster to train.
The flow of the algorithm[3][4] looks as follows: The update step is then formalized as a quadratic programming[2] problem: Find
, i.e. the score of the current correct training
must be greater than the score of any other possible
by at least the loss (number of errors) of that