Margin-infused relaxed algorithm
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:
Algorithm MIRA Input: Training examples Output: Set of parameters
← 0, ← 0 for ← 1 to for ← 1 to ← update according to ← end for end for return
- "←" denotes assignment. For instance, "largest ← item" means that the value of largest changes to the value of item.
- "return" terminates the algorithm and outputs the following value.
The update step is then formalized as a quadratic programming[2] 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 (number of errors) of that in comparison to .
References
- Crammer, Koby; Singer, Yoram (2003). "Ultraconservative Online Algorithms for Multiclass Problems". Journal of Machine Learning Research. 3: 951–991.
- McDonald, Ryan; Crammer, Koby; Pereira, Fernando (2005). "Online Large-Margin Training of Dependency Parsers" (PDF). Proceedings of the 43rd Annual Meeting of the ACL. Association for Computational Linguistics. pp. 91–98.
- Watanabe, T. et al (2007): "Online Large Margin Training for Statistical Machine Translation". In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 764–773.
- Bohnet, B. (2009): Efficient Parsing of Syntactic and Semantic Dependency Structures. Proceedings of Conference on Natural Language Learning (CoNLL), Boulder, 67–72.
External links
- adMIRAble - MIRA implementation in C++
- Miralium - MIRA implementation in Java
- MIRA implementation for Mahout in Hadoop