Margin-infused relaxed algorithm


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:
Input: Training examples
Output: Set of parameters
← 0, ← 0
for ← 1 to
for ← 1 to
← update according to
←
end for
end for
return
The update step is then formalized as a quadratic programming problem: Find, so that, i.e. the score of the current correct training must be greater than the score of any other possible by at least the loss of that in comparison to.