1 Answers
Margin-infused relaxed algorithm is a machine learning algorithm, an online algorithm for multiclass classification problems. It is designed to learn a set of parameters 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. The change of the parameters is kept as small as possible.
A two-class version called binary MIRA simplifies the algorithm by not requiring the solution of a quadratic programming problem. 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 looks as follows:
The update step is then formalized as a quadratic programming problem: Find m i n ‖ w − w ‖ {\displaystyle min\|w^{}-w^{}\|} , so that s c o r e − s c o r e ≥ L ∀ y ′ {\displaystyle score-score\geq L\ \forall y'} , i.e. the score of the current correct training y {\displaystyle y} must be greater than the score of any other possible y ′ {\displaystyle y'} by at least the loss of that y ′ {\displaystyle y'} in comparison to y {\displaystyle y}.